Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much re- search attention in recent year...Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much re- search attention in recent years. A shale＇s key geomechanical properties help to identify its ＂fracibility＂ its fluid flow patterns and rates, and its in-place petroleum resources and potential commercial re- serves. The models and the information they provide, in turn, enable engineers to design drilling pat- terns, fracture-stimulation programs and materials selection that will avoid formation damage and op- timize recovery of petroleum. A wide-range of tools, technologies, experiments and mathematical techniques are deployed to achieve this. Characterizing the interconnected fracture, permeability and porosity network is an essential step in understanding a shales highly-anisotropic features on multiple scales （nano to macro）. Weli-log data, and its petrophysical interpretation to calibrate many geome- chanical metrics to those measured in rock samples by laboratory techniques plays a key role in pro- viding affordable tools that can be deployed cost-effectively in multiple well bores. Likewise, micro- seismic data helps to match fracture density and propagation observed on a reservoir scale with pre- dictions from simulations and laboratory tests conducted on idealised/simplified discrete fracture net- work models. Shales complex wettability, adsorption and water imbibition characteristics have a sig- nificant influence on potential formation damage during stimulation and the short-term and long-term flow of petroleum achievable. Many gas flow mechanisms and models are proposed taking into ac- count the multiple flow mechanisms involved （e.g., desorption, diffusion, slippage and viscous flow op- erating at multiple porosity levels from nano- to macro-scales）. Fitting historical production data and well decline curves to model predictions helps to verify whether model＇s geomechanical assumptions are realistic or not. This review discusses the techniques applied and the models developed that are relevant to applied geomechanics, highlighting examples of their application and the numerous out- standin~ questions associated with them.展开更多
Discovered in 1949 with a rate of (195,000) bbl/day from pay zones in Mishrif and Zubair Formation, the expected production of Zubair field is anticipated to be 1125 million bbl/day. Despite this production history, t...Discovered in 1949 with a rate of (195,000) bbl/day from pay zones in Mishrif and Zubair Formation, the expected production of Zubair field is anticipated to be 1125 million bbl/day. Despite this production history, there is a major deficiency in detailed petrophysical analysis of the producing zones. In the present study well log data of 7 wells, selected from numerous wells, are investigated in details to examine the reservoir properties and characterize the reservoir architecture. The petrophysical analysis of Mishrif Formation indicated two or three pay zones. Lithologically, all zones of Mishrif Formation are dominantly clean limestone to dolomitic limestone with zone 2 and 3 reporting higher dolomitic content (20% to 40%) compared to zone 1 (6% to 13%). Mishrif pay zones indicated a relatively good porosity (18% - 24%) with zone 2 predominant in secondary porosity associating dolomitization processes. In Zubair Formation one pay zone is identified but locally could separate into two zones. The clay content is generally low with average content between 2% and 3% while the average porosity showed slightly better values in zone 1 (~0.20) compared to average porosity of zone 2 (0.17) that is rich in silt content associating deposition at a relatively deeper parts of the shelf. The average water saturation shows distinct lower values that vary between 15% and 18.7%. The petrophysical results are statistically analyzed and property histograms and crossplots are constructed to investigate mutual relationships. Such analysis is essential for understanding the reservoir architecture and calculations of reservoir capacity for future development.展开更多
Petrophysics of coals directly affects the development of coalbed methane(CBM).Based on the analysis of the representative academic works at home and abroad,the recent progress on petrophysics characteristics was revi...Petrophysics of coals directly affects the development of coalbed methane(CBM).Based on the analysis of the representative academic works at home and abroad,the recent progress on petrophysics characteristics was reviewed from the aspects of the scale-span porefracture structure,permeability,reservoir heterogeneity,and its controlling factors.The results showed that the characterization of pore-fracture has gone through three stages:qualitative and semiquantitative evaluation of porefracture by various techniques,quantitatively refined characterization of pore-fracture by integrating multiple methods including nuclear magnetic resonance analysis,liquid nitrogen,and mercury intrusion,and advanced quantitative characterization methods of pore-fracture by high-precision experimental instruments(focused-ion beam-scanning electron microscopy,small-angle neutron scattering and computed tomography scanner)and testing methods(m-CT scanning and X-ray diffraction).The effects of acoustic field can promote the diffusion of CBM and generally increase the permeability of coal reservoirs by more than 10%.For the controlling factors of reservoir petrophysics,tectonic stress is the most crucial factor in determining permeability,while the heterogeneity of CBM reservoirs increases with the enhancement of the tectonic deformation and stress field.The study on lithology heterogeneity of deep and high-dip coal measures,the spatial storage-seepage characteristics with deep CBM reservoirs,and the optimizing production between coal measures should be the leading research directions.展开更多
Artificial Intelligence,or AI,is a method of data analysis that learns from data,identify patterns and makes predictions with the minimal human intervention.AI is bringing many benefits to petrophysical evaluation.Usi...Artificial Intelligence,or AI,is a method of data analysis that learns from data,identify patterns and makes predictions with the minimal human intervention.AI is bringing many benefits to petrophysical evaluation.Using case studies,this paper describes several successful applications.The future of AI has even more potential.However,if used carelessly there are potentially grave consequences.A complex Middle East Carbonate field needed a bespoke shaly water saturation equation.AI was used to‘evolve’an ideal equation,together with field specific saturation and cementation exponents.One UKCS gas field had an‘oil problem’.Here,AI was used to unlock the hidden fluid information in the NMR T1 and T2 spectra and successfully differentiate oil and gas zones in real time.A North Sea field with 30 wells had shear velocity data(Vs)in only 4 wells.Vs was required for reservoir modelling and well bore stability prediction.AI was used to predict Vs in all 30 wells.Incorporating high vertical resolution data,the Vs predictions were even better than the recorded logs.As it is not economic to take core data on every well,AI is used to discover the relationships between logs,core,litho-facies and permeability in multi-dimensional data space.As a consequence,all wells in a field were populated with these data to build a robust reservoir model.In addition,the AI predicted data upscaled correctly unlike many conventional techniques.AI gives impressive results when automatically log quality controlling(LQC)and repairing electrical logs for bad hole and sections of missing data.AI doesn’t require prior knowledge of the petrophysical response equations and is self-calibrating.There are no parameters to pick or cross-plots to make.There is very little user intervention and AI avoids the problem of‘garbage in,garbage out’(GIGO),by ignoring noise and outliers.AI programs work with an unlimited number of electrical logs,core and gas chromatography data;and don’t‘fall-over’if some of those inputs are missing.AI programs currently being developed include ones where their machine code evolves using similar rules used by life’s DNA code.These AI programs pose considerable dangers far beyond the oil industry as described in this paper.A‘risk assessment’is essential on all AI programs so that all hazards and risk factors,that could cause harm,are identified and mitigated.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
Permeability is affected by complex factors such as the subsurface geological structure and porosity-permeability correlation.For highly heterogeneous reservoirs with complex pore structures,it is extremely challengin...Permeability is affected by complex factors such as the subsurface geological structure and porosity-permeability correlation.For highly heterogeneous reservoirs with complex pore structures,it is extremely challenging to spatially characterize(predict)permeability using seismic data.The conventional way of permeability prediction intends to convert underground refl ection data into the elastic parameters sensitive to underground fluids,build a universal low-dimensional template via petrophysical modeling and ultimately deliver spatial prediction of permeability.However,this method is restrained by the actual subsurface condition,selected well-logging sensitive parameters and the accuracy of the computed elastic parameters and fails to simulate the petrophysical mechanisms of complex reservoir permeability,which reduces the permeability prediction accuracy.The method proposed in this paper combines petrophysics and artificial intelligence and integrates multiple types of information to build the high-dimensional petrophysical template for permeability,in an attempt to improve the spatial characterization and prediction accuracy of permeability.The field testing demonstrates the high application performance and effective improvement in prediction accuracy and fluvial channel characterization.展开更多
The high-resolution nonlinear simultaneous inversion of petrophysical parameters is based on Bayesian statistics and combines petrophysics with geostatistical a priori information. We used the fast Fourier transform–...The high-resolution nonlinear simultaneous inversion of petrophysical parameters is based on Bayesian statistics and combines petrophysics with geostatistical a priori information. We used the fast Fourier transform–moving average（FFT–MA） and gradual deformation method（GDM） to obtain a reasonable variogram by using structural analysis and geostatistical a priori information of petrophysical parameters. Subsequently, we constructed the likelihood function according to the statistical petrophysical model. Finally, we used the Metropolis algorithm to sample the posteriori probability density and complete the inversion of the petrophysical parameters. We used the proposed method to process data from an oil fi eld in China and found good match between inversion and real data with high-resolution. In addition, the direct inversion of petrophysical parameters avoids the error accumulation and decreases the uncertainty, and increases the computational effi ciency.展开更多
Offshore Nile Delta in Egypt represents an enormous hydrocarbon province with recent projected gas and condensate discoveries of more than 50 trillion cubic feet“TCF”.Most of these occur in the post-salt hydrocarbon...Offshore Nile Delta in Egypt represents an enormous hydrocarbon province with recent projected gas and condensate discoveries of more than 50 trillion cubic feet“TCF”.Most of these occur in the post-salt hydrocarbon plays where biogenic gases are dominant.This study integrates organic geochemistry,seismic geomorphology and petrophysics in order to decipher the origin,and accumulation conditions of the wet gas/condensate blend in the Upper Miocene sub-salt Wakar Formation sandstones in Port Fouad Marine“PFM”Field,offshore Nile Delta.Hydrocarbon pay zones are scattered thin(<10 m)sandstones deposited in as turbiditic channel/levee complex facies.Spatial distribution of vertical gas chimneys(~2 km wide)rooting-down to the Messinian Rosetta salt is associated with the lateral pinching-out of the turbiditic sandstones.Organically-rich(total organic carbon“TOC”>1 w.t.%,hydrogen index“HI”>200 mgHC/gTOC)and mature(Tmax>430℃,vitrinite reflectance“VR”>0.6%R_(o)),source rocks are restricted to Upper Miocene Wakar and Oligo-Miocene Tineh formations.The latter contains more mature organofacies(up to 1.2%R_(o))and type Ⅱ/Ⅲ kerogen,thereby demonstrating a good capability to generate wet gases.The studied gas is wet and has thermogenic origin with signs of secondary microbial alteration,whereas the condensate contains a mixture of marine and terrestrial input.Molecular bio-markers of the condensate,isotopic and molecular composition of the gas reveals a generation of condensate prior to gas expulsion from the source.The Wakar sandstones have a heterogeneous pore system where three reservoir rock types(RRTⅠ,RRTⅡ and RRTⅢ).RRTI rocks present the bulk compo-sition of the Wakar pay zones.Spatial distribution of RRTⅠ facies likely control the accumulation of the sub-salt hydrocarbons.Our results provide a new evidence on an active petroleum system in the sub-salt Paleogene successions in the offshore Nile Delta where concomitant generation of gas/condensate blend has been outlined.展开更多
Natural gas hydrates are crystalline solid complexes with different morphologies found in marine sediments and permafrost zones. The petrophysical properties of gas hydrate-bearing sediments(GHBS) are crucial for unde...Natural gas hydrates are crystalline solid complexes with different morphologies found in marine sediments and permafrost zones. The petrophysical properties of gas hydrate-bearing sediments(GHBS) are crucial for understanding the characteristics of gas hydrate reservoirs, the spatial distribution of natural gas hydrates, and their exploitation potential. Geophysical exploration remains the primary approach for investigating the petrophysical properties of GHBS. However, limitations in resolution make it challenging to accurately characterize complex sediment structures, leading to difficulties in precisely interpreting petrophysical properties. Laboratory-based petrophysical experiments provide highly accurate results for petrophysical properties. Despite their accuracy, these experiments are costly, and difficulties in controlling variables may introduce uncertainties into geophysical exploration models.Advances in imaging and simulation techniques have established digital rock technology as an indispensable tool for enhancing petrophysical experimentation. This technology offers a novel microscopic perspective for elucidating the three-dimensional(3D) spatial distribution and multi-physical responses of GHBS. This paper presents an in-depth discussion of digital rock technology as applied to GHBS, with an emphasis on digital rock reconstruction and simulation of petrophysical properties. First, we summarize two common methods for constructing digital rocks of GHBS: petrophysical experimental methods and numerical reconstruction methods, followed by analyses of their respective advantages and limitations. Next, we delve into numerical simulation methods for evaluating GHBS petrophysical properties, including electrical, elastic, and fluid flow characteristics. Finally, we conduct a comprehensive analysis of the current trends in digital rock reconstruction and petrophysical simulation techniques for GHBS, emphasizing the necessity of multi-scale, multi-component, high-resolution 3D digital rock models to facilitate the precise characterization of complex gas hydrate reservoirs. Future applications of microscopic digital rock technology should be integrated with macroscopic geophysical exploration to enable more comprehensive and precise analyses of GHBS petrophysical properties.展开更多
Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for ...Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.展开更多
The Penobscot Field,located within the Scotian Basin offshore Nova Scotia,Canada,represents an underexplored hydrocarbon field with potential for future development.Previous studies have been confined to specific rese...The Penobscot Field,located within the Scotian Basin offshore Nova Scotia,Canada,represents an underexplored hydrocarbon field with potential for future development.Previous studies have been confined to specific reservoir intervals without integrating multiple stratigraphic levels,and a comprehensive static reservoir characterization and volumetric assessment of the Penobscot Field has yet to be undertaken,constraining its full development evaluation.This study presents a comprehensive characte rization of the field by integrating geological,geophysical,and petrophysical datasets,leading to static hydrocarbon reserve estimation.The workflow involves seismic interpretation,structural modeling,petrophysical evaluation,and static volumetric calculations.Seismic analysis revealed a structu rally complex setting dominated by normal and inverted faults,with reservoir intervals primarily within the Missisauga Formation,which is subdivided into upper,middle,and lower units.Petro p hysical evaluation from well logs and core data identified key reservoir properties,including porosity ranging from 12 % to 28 %,permeability spanning from 1 to 1000 mD,and variable water saturations.Stochastic modeling of facies and petro p hysical attributes provided insights into lateral and ve rtical hete rogeneity.The Penobscot Field's original oil-in-place ranges from 41.6×10~6 m3 to 109.7×10~6 m3,with the Middle Missisauga sands presenting the highest reservoir potential.Fault seal analysis indicated predominantly sealing behavior in the shallow sections and semi-permeable conditions at greater depths,suggesting potential lateral migration pathways.The results underscore the field's hydrocarbon potential while emphasizing the significance of structural complexity,facies distribution,and petrophysical variability in reservoir quality,as well as its potential for future development or utilization of similar sand reservoirs for CO_(2) storage utilization.This work provides the first fully integrated static reservoir model of the Penobscot Field,offering critical insights for delineating the hydrocarbon reservoirs potential and future production strategies in the Scotian Basin.展开更多
Accurate prediction of reservoir parameters is essential for reservoir evaluation.Recent machine learning methods have spurred significant advancements in reservoir prediction;however,limited well logging data and str...Accurate prediction of reservoir parameters is essential for reservoir evaluation.Recent machine learning methods have spurred significant advancements in reservoir prediction;however,limited well logging data and strong reservoir heterogeneity still hinder the accuracy and reliability of such predictions.Addressing these challenges requires methods capable of effectively predicting reservoir parameters under data scarcity and complex reservoir structures.In this study,we propose CAF2,a feature-fusion cross-modal alignment framework guided by large language models(LLMs).CAF2 integrates data augmentation,knowledge distillation,cross-modal alignment,and feature fusion.The data augmentation module employs the RealTabFormer model to generate synthetic datasets that mirror the distribution of real logging data,addressing the challenge of data scarcity.Knowledge distillation extracts essential domain knowledge from LLMs,guiding cross-modal alignment between well logging data and textual knowledge.This alignment mitigates modality gaps via token and sequence alignment,enhancing depth-domain feature representation.Finally,a cross-variable and cross-depth feature fusion strategy exploits both variable information and depth correlations,overcoming the difficulties in accurate reservoir parameter prediction posed by reservoir heterogeneity.Experimental results demonstrate that CAF2 significantly outperforms existing models in predicting tight sandstone reservoir parameters,serving as an effective tool for oil and gas exploration and development.展开更多
The shear wave(S-wave)velocity is a critical rock elastic parameter in shale reservoirs,especially for evaluating shale fracability.To effectively supplement S-wave velocity under the condition of no actual measuremen...The shear wave(S-wave)velocity is a critical rock elastic parameter in shale reservoirs,especially for evaluating shale fracability.To effectively supplement S-wave velocity under the condition of no actual measurement data,this paper proposes a physically-data driven method for the S-wave velocity prediction in shale reservoirs based on the class activation mapping(CAM)technique combined with a physically constrained two-dimensional Convolutional Neural Network(2D-CNN).High-sensitivity log curves related to S-wave velocity are selected as the basis from the data sensitivity analysis.Then,we establish a petrophysical model of complex multi-mineral components based on the petrophysical properties of porous medium and the Biot-Gassmann equation.This model can help reduce the dispersion effect and constrain the 2D-CNN.In deep learning,the 2D-CNN model is optimized using the Adam,and the class activation maps(CAMs)are obtained by replacing the fully connected layer with the global average pooling(GAP)layer,resulting in explainable results.The model is then applied to wells A,B1,and B2 in the southern Songliao Basin,China and compared with the unconstrained model and the petrophysical model.The results show higher prediction accuracy and generalization ability,as evidenced by correlation coefficients and relative errors of 0.98 and 2.14%,0.97 and 2.35%,0.96 and 2.89%in the three test wells,respectively.Finally,we present the defined C-factor as a means of evaluating the extent of concern regarding CAMs in regression problems.When the results of the petrophysical model are added to the 2D feature maps,the C-factor values are significantly increased,indicating that the focus of 2D-CNN can be significantly enhanced by incorporating the petrophysical model,thereby imposing physical constraints on the 2D-CNN.In addition,we establish the SHAP model,and the results of the petrophysical model have the highest average SHAP values across the three test wells.This helps to assist in proving the importance of constraints.展开更多
The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogene...The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.展开更多
It is of great significance to evaluate the petrophysical properties in shale oil reservoir,which can contribute to geological storage CO_(2).Two-dimensional nuclear magnetic resonance(2D NMR)technology has been appli...It is of great significance to evaluate the petrophysical properties in shale oil reservoir,which can contribute to geological storage CO_(2).Two-dimensional nuclear magnetic resonance(2D NMR)technology has been applied to petrophysical characterization in shale oil reservoir.However,limitations of traditional 2D NMR(T_(1)-T_(2)or T_(2)-D)in detecting short-lived organic matter and the complexity of mineral compositions,pose NMR-based petrophysical challenges.The organic pores were assumed saturated oil and the inorganic pores were assumed saturated water,and the numerical algorithm and theory of T_(1)-T_(2)^(*)in shale oil reservoir were proposed,whose accuracy was validated through T_(2),T_(1)-T_(2)and T_(2)^(*)experiments.The effects of mineral types and contents on the T_(1)-T_(2)^(*)responses were firstly simulated by the random walk algorithm,revealing the NMR response mechanisms in shale oil reservoir with complex mineral compositions at different magnetic field frequency(f).The results indicate that when the pyrite content is 5.43%,dwell time is 4μs,the f is 200 MHz,and echo spacing is 0.4 ms,the T_(1)-T_(2)^(*)-based porosity is 2.39 times that of T_(1)-T_(2)-based porosity.The T_(2LM)^(*)is 0.015 ms,which is 0.015 times that of T_(2)LM.The T_(1LM)is 8.84 ms,which is 0.63 times that of T_(1LM).The T_(1)-T_(2)^(*)-based petrophysical conversion models were firstly created,and the foundation of petrophysical conversion was laid at different f.展开更多
Shale needs to contain a sufficient amount of gas to make it viable for exploitation. The continental heterogeneous shale formation in the Yan-chang （YC） area is investigated by firstly measuring the shale gas conte...Shale needs to contain a sufficient amount of gas to make it viable for exploitation. The continental heterogeneous shale formation in the Yan-chang （YC） area is investigated by firstly measuring the shale gas content in a laboratory and then investigating use of a theoretical prediction model. Key factors controlling the shale gas content are determined, and a prediction model for free gas content is established according to the equation of gas state and a new petrophysical volume model. Application of the Langmuir volume constant and pressure constant obtained from results of adsorption isotherms is found to be limited because these constants are greatly affected by experimental temperature and pressures. Therefore, using measurements of adsorption isotherms and thermodynamic theory, the influence of temperature, total organic carbon （TOC）, and mineralogy on Langmuir volume constants and pressure constants are investigated in detail. A prediction model for the Langmuir pressure constant with a correction of temperatures is then established, and a prediction model for the Langmuir volume constant with correction of temperature, TOC, and quartz contents is also proposed. Using these corrected Langmuir constants, application of the Langmuir model determined using experimental adsorption isotherms is extrapolated to reservoir temperature, pressure, and lithological conditions, and a method for the prediction of shale gas content using well logs is established. Finally, this method is successfully applied to predict the shale gas content of the continental shale formation in the YC area, and practical application is shown to deliver good results with high precision.展开更多
As a parameter that describes heat transmission properties of rocks,thermal conductivity is indispensable for studying the thermal regime of sedimentary basins,and retrieving high-quality data of thermal conductivity ...As a parameter that describes heat transmission properties of rocks,thermal conductivity is indispensable for studying the thermal regime of sedimentary basins,and retrieving high-quality data of thermal conductivity is the basis for geothermal related studies.The optical scanning method is used here to measure the thermal conductivity of 745 drill-core samples from the Tarim basin,the largest intermontane basin with abundant hydrocarbon potential in China,and water saturation correction is made for clastic rock samples that are of variable porosity.All the measured values,combined with previously published data in this area,are integrated to discuss the distribution characteristics and major controlling factors that affect the thermal conductivity of rocks in the basin.Our results show that the values of thermal conductivity of rocks generally range from 1.500 to 3.000 W/m·K with a mean of 2.304 W/m·K.Thermal conductivity differs considerably between lithological types：the value of a coal sample is found to be the lowest as being only 0.249 W/m·K,while the values for salt rock samples are the highest with a mean of 4.620 W/m·K.Additionally,it is also found that the thermal conductivity of the same or similar lithologic types shows considerable differences,suggesting that thermal conductivity cannot be used for distinguishing the rock types.The thermal conductivity values of mudstone and sandstone generally increase with increasing burial depth and geological age of the formation,reflecting the effect of porosity of rocks on thermal conductivity.In general,the mineral composition,fabric and porosity of rocks are the main factors that affect the thermal conductivity.The research also reveals that the obvious contrast in thermal conductivity of coal and salt rock with other common sedimentary rocks can induce subsurface temperature anomalies in the overlying and underlying formations,which can modify the thermal evolution and maturity of the source rocks concerned.This finding is very important for oil and gas resources assessment and exploration and needs further study in detail.The results reported here are representative of the latest and most complete dataset of thermal conductivity of rocks in the Tarim basin,and will provide a solid foundation for geothermal studies in future.展开更多
Understanding and modelling the wettability of tight rocks is essential for designing fracturing and treatment fluids. In this paper, we measure and analyze spontaneous imbibition of water and oil into five twin core ...Understanding and modelling the wettability of tight rocks is essential for designing fracturing and treatment fluids. In this paper, we measure and analyze spontaneous imbibition of water and oil into five twin core plugs drilled from the cores of a well drilled in the Montney Formation, an unconventional oil and gas play in the Western Canadian Sedimentary Basin. We characterize the samples by measuring the mineralogy using XRD（x-ray diffraction）, total organic carbon content, porosity, and permeability. Interestingly, the equilibrated water uptake of the five samples is similar, while, their oil uptake increases by increasing the core porosity and permeability. We define two wettability indices for the oil phase based on the slope and equilibrium values of water and oil imbibition curves. Both indices increase by increasing porosity and permeability, with the slope affinity index showing a stronger correlation. This observation suggests that part of the pore network has a stronger affinity to oil than to water. We also observe that the two indices decrease by increasing neutron porosity and gamma ray parameters measured by wireline logging tools. The samples with higher gamma ray and neutron porosity are expected to have greater clay content, and thus less effective porosity and permeability.展开更多
The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin,Australia.This study involved combination methods such as petrophysical analysis,petrography a...The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin,Australia.This study involved combination methods such as petrophysical analysis,petrography and sedimentological studies,reservoir quality and fluid flow units from derivative parameters,and capillary pressure and wetting fluid saturation relationship.Textural and diagenetic features are affecting the reservoir quality.Cementation,compaction,and presence of clay minerals such as kaolinite are found to reduce the quality while dissolution and secondary porosity are noticed to improve it.It is believed that the Narimba Formation is a potential reservoir with a wide range of porosity and permeability.Porosity ranges from 3.1%to 25.4%with a mean of 15.84%,while permeability ranges between 0.01 mD and 510 mD,with a mean of 31.05 mD.Based on the heterogenous lithology,the formation has been categorized into five groups based on permeability variations.Group I showed an excellent to good quality reservoir with coarse grains.The impacts of both textural and diagenetic features improve the reservoir and producing higher reservoir quality index(RQI)and flow zone indicators(FZI)as well as mostly mega pores.The non-wetting fluid migration has the higher possibility to flow in the formation while displacement pressure recorded as zero.Group II showed a fair quality reservoir with lower petrophysical properties in macro pores.The irreducible water saturation is increasing while the textural and digenetic properties are still enhancing the reservoir quality.Group III reflects lower quality reservoir with mostly macro pores and higher displacement pressure.It may indicate smaller grain size and increasing amount of cement and clay minerals.Group IV,and V are interpreted as a poor-quality reservoir that has lower RQI and FZI.The textural and digenetic features are negatively affecting the reservoir and are leading to smaller pore size and pore throat radii(r35)values to be within the range of macro,meso-,micro-,and nano pores.The capillary displacement pressure curves of the three groups show increases reaching the maximum value of 400 psia in group V.Agreement with the classification of permeability,r35 values,and pore type can be used in identifying the quality of reservoir.展开更多
The Eocene Sokor1 Formation is proven oil reservoir rocks in the Termit sub-basin. These sandstone intervals are deeply buried, highly heterogeneous in character and characterized by Low Contrast Low Resistivity (LCLR...The Eocene Sokor1 Formation is proven oil reservoir rocks in the Termit sub-basin. These sandstone intervals are deeply buried, highly heterogeneous in character and characterized by Low Contrast Low Resistivity (LCLR) log responses. Petrophysical and quantitative well-based rock physics interpretations were integrated for property estimations, fluid and lithology typing in reservoir characterization. Six (6) reservoir sandstone intervals were identified, delineated and correlated across five (5) wells. The estimated petrophysical properties showed that the Eocene Sokor1 sandstones have averagely good reservoir properties with sand_5 interval exhibiting exceptional reservoir properties. <i><span style="font-family:Verdana;">V</span><sub><span style="font-family:Verdana;">p</span></sub><span style="font-family:Verdana;">/V</span><sub><span style="font-family:Verdana;">s</span></sub></i><span style="font-family:Verdana;"> vs. AI and </span><i><span style="font-family:Verdana;">μρ</span></i><span style="font-family:Verdana;"> vs. </span><i><span style="font-family:Verdana;">λρ</span></i><span style="font-family:Verdana;"> elastic cross-plots color coded with reservoir properties (</span><i><span style="font-family:Verdana;">V</span><sub><span style="font-family:Verdana;">sh</span></sub></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;"><span style="white-space:nowrap;">Φ</span></span></i><span style="font-family:Verdana;">), show distinct and well separated data clusters signifying hydrocarbon bearing sandstones, brine sandstones and shales/mudstones in the 3D crossplot planes with varying seismic elastic property values in each well thereby, enhancing reservoir characterization and providing information’s about the burial history, reservoir quality and property distribution in the sub-basin. The analys</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> suggests that, although the reservoir interval has averagely good petrophysical properties in all wells, the seismic elastic crossplots show that these properties are much better distributed in wells 2 and 3 than in wells 4, 5 and 9. Therefore, sand_5 reservoir interval in wells 2 and 3 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> likely to be more hydrocarbon bearing and productive than wells 4, 5 and 9 in the sub-basin.</span></span></span>展开更多
基金the Department of Science & Technology (DST Ministry of Science & Technology, Government of India), for providing funding for his research through the DST-Inspire Assured Opportunity of Research Career (AORC) scheme
文摘Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much re- search attention in recent years. A shale＇s key geomechanical properties help to identify its ＂fracibility＂ its fluid flow patterns and rates, and its in-place petroleum resources and potential commercial re- serves. The models and the information they provide, in turn, enable engineers to design drilling pat- terns, fracture-stimulation programs and materials selection that will avoid formation damage and op- timize recovery of petroleum. A wide-range of tools, technologies, experiments and mathematical techniques are deployed to achieve this. Characterizing the interconnected fracture, permeability and porosity network is an essential step in understanding a shales highly-anisotropic features on multiple scales （nano to macro）. Weli-log data, and its petrophysical interpretation to calibrate many geome- chanical metrics to those measured in rock samples by laboratory techniques plays a key role in pro- viding affordable tools that can be deployed cost-effectively in multiple well bores. Likewise, micro- seismic data helps to match fracture density and propagation observed on a reservoir scale with pre- dictions from simulations and laboratory tests conducted on idealised/simplified discrete fracture net- work models. Shales complex wettability, adsorption and water imbibition characteristics have a sig- nificant influence on potential formation damage during stimulation and the short-term and long-term flow of petroleum achievable. Many gas flow mechanisms and models are proposed taking into ac- count the multiple flow mechanisms involved （e.g., desorption, diffusion, slippage and viscous flow op- erating at multiple porosity levels from nano- to macro-scales）. Fitting historical production data and well decline curves to model predictions helps to verify whether model＇s geomechanical assumptions are realistic or not. This review discusses the techniques applied and the models developed that are relevant to applied geomechanics, highlighting examples of their application and the numerous out- standin~ questions associated with them.
文摘Discovered in 1949 with a rate of (195,000) bbl/day from pay zones in Mishrif and Zubair Formation, the expected production of Zubair field is anticipated to be 1125 million bbl/day. Despite this production history, there is a major deficiency in detailed petrophysical analysis of the producing zones. In the present study well log data of 7 wells, selected from numerous wells, are investigated in details to examine the reservoir properties and characterize the reservoir architecture. The petrophysical analysis of Mishrif Formation indicated two or three pay zones. Lithologically, all zones of Mishrif Formation are dominantly clean limestone to dolomitic limestone with zone 2 and 3 reporting higher dolomitic content (20% to 40%) compared to zone 1 (6% to 13%). Mishrif pay zones indicated a relatively good porosity (18% - 24%) with zone 2 predominant in secondary porosity associating dolomitization processes. In Zubair Formation one pay zone is identified but locally could separate into two zones. The clay content is generally low with average content between 2% and 3% while the average porosity showed slightly better values in zone 1 (~0.20) compared to average porosity of zone 2 (0.17) that is rich in silt content associating deposition at a relatively deeper parts of the shelf. The average water saturation shows distinct lower values that vary between 15% and 18.7%. The petrophysical results are statistically analyzed and property histograms and crossplots are constructed to investigate mutual relationships. Such analysis is essential for understanding the reservoir architecture and calculations of reservoir capacity for future development.
基金funded by the National Natural Science Foundation of China(Grant Nos.41830427,41772160 and 41922016)。
文摘Petrophysics of coals directly affects the development of coalbed methane(CBM).Based on the analysis of the representative academic works at home and abroad,the recent progress on petrophysics characteristics was reviewed from the aspects of the scale-span porefracture structure,permeability,reservoir heterogeneity,and its controlling factors.The results showed that the characterization of pore-fracture has gone through three stages:qualitative and semiquantitative evaluation of porefracture by various techniques,quantitatively refined characterization of pore-fracture by integrating multiple methods including nuclear magnetic resonance analysis,liquid nitrogen,and mercury intrusion,and advanced quantitative characterization methods of pore-fracture by high-precision experimental instruments(focused-ion beam-scanning electron microscopy,small-angle neutron scattering and computed tomography scanner)and testing methods(m-CT scanning and X-ray diffraction).The effects of acoustic field can promote the diffusion of CBM and generally increase the permeability of coal reservoirs by more than 10%.For the controlling factors of reservoir petrophysics,tectonic stress is the most crucial factor in determining permeability,while the heterogeneity of CBM reservoirs increases with the enhancement of the tectonic deformation and stress field.The study on lithology heterogeneity of deep and high-dip coal measures,the spatial storage-seepage characteristics with deep CBM reservoirs,and the optimizing production between coal measures should be the leading research directions.
文摘Artificial Intelligence,or AI,is a method of data analysis that learns from data,identify patterns and makes predictions with the minimal human intervention.AI is bringing many benefits to petrophysical evaluation.Using case studies,this paper describes several successful applications.The future of AI has even more potential.However,if used carelessly there are potentially grave consequences.A complex Middle East Carbonate field needed a bespoke shaly water saturation equation.AI was used to‘evolve’an ideal equation,together with field specific saturation and cementation exponents.One UKCS gas field had an‘oil problem’.Here,AI was used to unlock the hidden fluid information in the NMR T1 and T2 spectra and successfully differentiate oil and gas zones in real time.A North Sea field with 30 wells had shear velocity data(Vs)in only 4 wells.Vs was required for reservoir modelling and well bore stability prediction.AI was used to predict Vs in all 30 wells.Incorporating high vertical resolution data,the Vs predictions were even better than the recorded logs.As it is not economic to take core data on every well,AI is used to discover the relationships between logs,core,litho-facies and permeability in multi-dimensional data space.As a consequence,all wells in a field were populated with these data to build a robust reservoir model.In addition,the AI predicted data upscaled correctly unlike many conventional techniques.AI gives impressive results when automatically log quality controlling(LQC)and repairing electrical logs for bad hole and sections of missing data.AI doesn’t require prior knowledge of the petrophysical response equations and is self-calibrating.There are no parameters to pick or cross-plots to make.There is very little user intervention and AI avoids the problem of‘garbage in,garbage out’(GIGO),by ignoring noise and outliers.AI programs work with an unlimited number of electrical logs,core and gas chromatography data;and don’t‘fall-over’if some of those inputs are missing.AI programs currently being developed include ones where their machine code evolves using similar rules used by life’s DNA code.These AI programs pose considerable dangers far beyond the oil industry as described in this paper.A‘risk assessment’is essential on all AI programs so that all hazards and risk factors,that could cause harm,are identified and mitigated.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
文摘Permeability is affected by complex factors such as the subsurface geological structure and porosity-permeability correlation.For highly heterogeneous reservoirs with complex pore structures,it is extremely challenging to spatially characterize(predict)permeability using seismic data.The conventional way of permeability prediction intends to convert underground refl ection data into the elastic parameters sensitive to underground fluids,build a universal low-dimensional template via petrophysical modeling and ultimately deliver spatial prediction of permeability.However,this method is restrained by the actual subsurface condition,selected well-logging sensitive parameters and the accuracy of the computed elastic parameters and fails to simulate the petrophysical mechanisms of complex reservoir permeability,which reduces the permeability prediction accuracy.The method proposed in this paper combines petrophysics and artificial intelligence and integrates multiple types of information to build the high-dimensional petrophysical template for permeability,in an attempt to improve the spatial characterization and prediction accuracy of permeability.The field testing demonstrates the high application performance and effective improvement in prediction accuracy and fluvial channel characterization.
基金sponsored by the National Basic Research Program of China(No.2013CB228604)the Major National Science and Technology Projects(No.2011ZX05009)+1 种基金the Natural Science Foundation of Shandong Province(No.ZR2011DQ013)the National Science Foundation of China(No.41204085)
文摘The high-resolution nonlinear simultaneous inversion of petrophysical parameters is based on Bayesian statistics and combines petrophysics with geostatistical a priori information. We used the fast Fourier transform–moving average（FFT–MA） and gradual deformation method（GDM） to obtain a reasonable variogram by using structural analysis and geostatistical a priori information of petrophysical parameters. Subsequently, we constructed the likelihood function according to the statistical petrophysical model. Finally, we used the Metropolis algorithm to sample the posteriori probability density and complete the inversion of the petrophysical parameters. We used the proposed method to process data from an oil fi eld in China and found good match between inversion and real data with high-resolution. In addition, the direct inversion of petrophysical parameters avoids the error accumulation and decreases the uncertainty, and increases the computational effi ciency.
文摘Offshore Nile Delta in Egypt represents an enormous hydrocarbon province with recent projected gas and condensate discoveries of more than 50 trillion cubic feet“TCF”.Most of these occur in the post-salt hydrocarbon plays where biogenic gases are dominant.This study integrates organic geochemistry,seismic geomorphology and petrophysics in order to decipher the origin,and accumulation conditions of the wet gas/condensate blend in the Upper Miocene sub-salt Wakar Formation sandstones in Port Fouad Marine“PFM”Field,offshore Nile Delta.Hydrocarbon pay zones are scattered thin(<10 m)sandstones deposited in as turbiditic channel/levee complex facies.Spatial distribution of vertical gas chimneys(~2 km wide)rooting-down to the Messinian Rosetta salt is associated with the lateral pinching-out of the turbiditic sandstones.Organically-rich(total organic carbon“TOC”>1 w.t.%,hydrogen index“HI”>200 mgHC/gTOC)and mature(Tmax>430℃,vitrinite reflectance“VR”>0.6%R_(o)),source rocks are restricted to Upper Miocene Wakar and Oligo-Miocene Tineh formations.The latter contains more mature organofacies(up to 1.2%R_(o))and type Ⅱ/Ⅲ kerogen,thereby demonstrating a good capability to generate wet gases.The studied gas is wet and has thermogenic origin with signs of secondary microbial alteration,whereas the condensate contains a mixture of marine and terrestrial input.Molecular bio-markers of the condensate,isotopic and molecular composition of the gas reveals a generation of condensate prior to gas expulsion from the source.The Wakar sandstones have a heterogeneous pore system where three reservoir rock types(RRTⅠ,RRTⅡ and RRTⅢ).RRTI rocks present the bulk compo-sition of the Wakar pay zones.Spatial distribution of RRTⅠ facies likely control the accumulation of the sub-salt hydrocarbons.Our results provide a new evidence on an active petroleum system in the sub-salt Paleogene successions in the offshore Nile Delta where concomitant generation of gas/condensate blend has been outlined.
基金the National Key R&D Program of China(2023YEE0119900)National Natural Science Foundation of China(Nos.92058211,42204105 and 42121005)+4 种基金Fundamental Research Funds for the Central Universities(No.862201013140)111 project(No.B20048)the International(Regional)Cooperation and Exchange Programs(No.12411530092)the Young Talent Fund of Association for Science and Technology in Shaanxi(No.20230703)Technology Innovation Leading Program of Shaanxi(No.2024 ZC-YYDP-27).
文摘Natural gas hydrates are crystalline solid complexes with different morphologies found in marine sediments and permafrost zones. The petrophysical properties of gas hydrate-bearing sediments(GHBS) are crucial for understanding the characteristics of gas hydrate reservoirs, the spatial distribution of natural gas hydrates, and their exploitation potential. Geophysical exploration remains the primary approach for investigating the petrophysical properties of GHBS. However, limitations in resolution make it challenging to accurately characterize complex sediment structures, leading to difficulties in precisely interpreting petrophysical properties. Laboratory-based petrophysical experiments provide highly accurate results for petrophysical properties. Despite their accuracy, these experiments are costly, and difficulties in controlling variables may introduce uncertainties into geophysical exploration models.Advances in imaging and simulation techniques have established digital rock technology as an indispensable tool for enhancing petrophysical experimentation. This technology offers a novel microscopic perspective for elucidating the three-dimensional(3D) spatial distribution and multi-physical responses of GHBS. This paper presents an in-depth discussion of digital rock technology as applied to GHBS, with an emphasis on digital rock reconstruction and simulation of petrophysical properties. First, we summarize two common methods for constructing digital rocks of GHBS: petrophysical experimental methods and numerical reconstruction methods, followed by analyses of their respective advantages and limitations. Next, we delve into numerical simulation methods for evaluating GHBS petrophysical properties, including electrical, elastic, and fluid flow characteristics. Finally, we conduct a comprehensive analysis of the current trends in digital rock reconstruction and petrophysical simulation techniques for GHBS, emphasizing the necessity of multi-scale, multi-component, high-resolution 3D digital rock models to facilitate the precise characterization of complex gas hydrate reservoirs. Future applications of microscopic digital rock technology should be integrated with macroscopic geophysical exploration to enable more comprehensive and precise analyses of GHBS petrophysical properties.
基金the support of Research Program of Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010the Sichuan Science and Technology Program under Grant 2024NSFSC1984 and Grant 2024NSFSC1990。
文摘Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.
文摘The Penobscot Field,located within the Scotian Basin offshore Nova Scotia,Canada,represents an underexplored hydrocarbon field with potential for future development.Previous studies have been confined to specific reservoir intervals without integrating multiple stratigraphic levels,and a comprehensive static reservoir characterization and volumetric assessment of the Penobscot Field has yet to be undertaken,constraining its full development evaluation.This study presents a comprehensive characte rization of the field by integrating geological,geophysical,and petrophysical datasets,leading to static hydrocarbon reserve estimation.The workflow involves seismic interpretation,structural modeling,petrophysical evaluation,and static volumetric calculations.Seismic analysis revealed a structu rally complex setting dominated by normal and inverted faults,with reservoir intervals primarily within the Missisauga Formation,which is subdivided into upper,middle,and lower units.Petro p hysical evaluation from well logs and core data identified key reservoir properties,including porosity ranging from 12 % to 28 %,permeability spanning from 1 to 1000 mD,and variable water saturations.Stochastic modeling of facies and petro p hysical attributes provided insights into lateral and ve rtical hete rogeneity.The Penobscot Field's original oil-in-place ranges from 41.6×10~6 m3 to 109.7×10~6 m3,with the Middle Missisauga sands presenting the highest reservoir potential.Fault seal analysis indicated predominantly sealing behavior in the shallow sections and semi-permeable conditions at greater depths,suggesting potential lateral migration pathways.The results underscore the field's hydrocarbon potential while emphasizing the significance of structural complexity,facies distribution,and petrophysical variability in reservoir quality,as well as its potential for future development or utilization of similar sand reservoirs for CO_(2) storage utilization.This work provides the first fully integrated static reservoir model of the Penobscot Field,offering critical insights for delineating the hydrocarbon reservoirs potential and future production strategies in the Scotian Basin.
基金supported by the Special Funds for Science and Technology Planning Projects of Sichuan Province(No.2024YFHZ0158)。
文摘Accurate prediction of reservoir parameters is essential for reservoir evaluation.Recent machine learning methods have spurred significant advancements in reservoir prediction;however,limited well logging data and strong reservoir heterogeneity still hinder the accuracy and reliability of such predictions.Addressing these challenges requires methods capable of effectively predicting reservoir parameters under data scarcity and complex reservoir structures.In this study,we propose CAF2,a feature-fusion cross-modal alignment framework guided by large language models(LLMs).CAF2 integrates data augmentation,knowledge distillation,cross-modal alignment,and feature fusion.The data augmentation module employs the RealTabFormer model to generate synthetic datasets that mirror the distribution of real logging data,addressing the challenge of data scarcity.Knowledge distillation extracts essential domain knowledge from LLMs,guiding cross-modal alignment between well logging data and textual knowledge.This alignment mitigates modality gaps via token and sequence alignment,enhancing depth-domain feature representation.Finally,a cross-variable and cross-depth feature fusion strategy exploits both variable information and depth correlations,overcoming the difficulties in accurate reservoir parameter prediction posed by reservoir heterogeneity.Experimental results demonstrate that CAF2 significantly outperforms existing models in predicting tight sandstone reservoir parameters,serving as an effective tool for oil and gas exploration and development.
基金supported by the National Natural Science Foundation of China(Nos.42374150,42374152)Natural Science Foundation of Shandong Province(ZR2020MD050).
文摘The shear wave(S-wave)velocity is a critical rock elastic parameter in shale reservoirs,especially for evaluating shale fracability.To effectively supplement S-wave velocity under the condition of no actual measurement data,this paper proposes a physically-data driven method for the S-wave velocity prediction in shale reservoirs based on the class activation mapping(CAM)technique combined with a physically constrained two-dimensional Convolutional Neural Network(2D-CNN).High-sensitivity log curves related to S-wave velocity are selected as the basis from the data sensitivity analysis.Then,we establish a petrophysical model of complex multi-mineral components based on the petrophysical properties of porous medium and the Biot-Gassmann equation.This model can help reduce the dispersion effect and constrain the 2D-CNN.In deep learning,the 2D-CNN model is optimized using the Adam,and the class activation maps(CAMs)are obtained by replacing the fully connected layer with the global average pooling(GAP)layer,resulting in explainable results.The model is then applied to wells A,B1,and B2 in the southern Songliao Basin,China and compared with the unconstrained model and the petrophysical model.The results show higher prediction accuracy and generalization ability,as evidenced by correlation coefficients and relative errors of 0.98 and 2.14%,0.97 and 2.35%,0.96 and 2.89%in the three test wells,respectively.Finally,we present the defined C-factor as a means of evaluating the extent of concern regarding CAMs in regression problems.When the results of the petrophysical model are added to the 2D feature maps,the C-factor values are significantly increased,indicating that the focus of 2D-CNN can be significantly enhanced by incorporating the petrophysical model,thereby imposing physical constraints on the 2D-CNN.In addition,we establish the SHAP model,and the results of the petrophysical model have the highest average SHAP values across the three test wells.This helps to assist in proving the importance of constraints.
文摘The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.
基金funded by the National Natural Science Foundation of China(42174131)。
文摘It is of great significance to evaluate the petrophysical properties in shale oil reservoir,which can contribute to geological storage CO_(2).Two-dimensional nuclear magnetic resonance(2D NMR)technology has been applied to petrophysical characterization in shale oil reservoir.However,limitations of traditional 2D NMR(T_(1)-T_(2)or T_(2)-D)in detecting short-lived organic matter and the complexity of mineral compositions,pose NMR-based petrophysical challenges.The organic pores were assumed saturated oil and the inorganic pores were assumed saturated water,and the numerical algorithm and theory of T_(1)-T_(2)^(*)in shale oil reservoir were proposed,whose accuracy was validated through T_(2),T_(1)-T_(2)and T_(2)^(*)experiments.The effects of mineral types and contents on the T_(1)-T_(2)^(*)responses were firstly simulated by the random walk algorithm,revealing the NMR response mechanisms in shale oil reservoir with complex mineral compositions at different magnetic field frequency(f).The results indicate that when the pyrite content is 5.43%,dwell time is 4μs,the f is 200 MHz,and echo spacing is 0.4 ms,the T_(1)-T_(2)^(*)-based porosity is 2.39 times that of T_(1)-T_(2)-based porosity.The T_(2LM)^(*)is 0.015 ms,which is 0.015 times that of T_(2)LM.The T_(1LM)is 8.84 ms,which is 0.63 times that of T_(1LM).The T_(1)-T_(2)^(*)-based petrophysical conversion models were firstly created,and the foundation of petrophysical conversion was laid at different f.
基金sponsored by National Natural Science Foundation(Project number:41274136)
文摘Shale needs to contain a sufficient amount of gas to make it viable for exploitation. The continental heterogeneous shale formation in the Yan-chang （YC） area is investigated by firstly measuring the shale gas content in a laboratory and then investigating use of a theoretical prediction model. Key factors controlling the shale gas content are determined, and a prediction model for free gas content is established according to the equation of gas state and a new petrophysical volume model. Application of the Langmuir volume constant and pressure constant obtained from results of adsorption isotherms is found to be limited because these constants are greatly affected by experimental temperature and pressures. Therefore, using measurements of adsorption isotherms and thermodynamic theory, the influence of temperature, total organic carbon （TOC）, and mineralogy on Langmuir volume constants and pressure constants are investigated in detail. A prediction model for the Langmuir pressure constant with a correction of temperatures is then established, and a prediction model for the Langmuir volume constant with correction of temperature, TOC, and quartz contents is also proposed. Using these corrected Langmuir constants, application of the Langmuir model determined using experimental adsorption isotherms is extrapolated to reservoir temperature, pressure, and lithological conditions, and a method for the prediction of shale gas content using well logs is established. Finally, this method is successfully applied to predict the shale gas content of the continental shale formation in the YC area, and practical application is shown to deliver good results with high precision.
基金supported by the National Natural Science Foundation of China(Nos40504013 and 40634021)Young Scientists Innovation Project of CNPC(No07E1033)
文摘As a parameter that describes heat transmission properties of rocks,thermal conductivity is indispensable for studying the thermal regime of sedimentary basins,and retrieving high-quality data of thermal conductivity is the basis for geothermal related studies.The optical scanning method is used here to measure the thermal conductivity of 745 drill-core samples from the Tarim basin,the largest intermontane basin with abundant hydrocarbon potential in China,and water saturation correction is made for clastic rock samples that are of variable porosity.All the measured values,combined with previously published data in this area,are integrated to discuss the distribution characteristics and major controlling factors that affect the thermal conductivity of rocks in the basin.Our results show that the values of thermal conductivity of rocks generally range from 1.500 to 3.000 W/m·K with a mean of 2.304 W/m·K.Thermal conductivity differs considerably between lithological types：the value of a coal sample is found to be the lowest as being only 0.249 W/m·K,while the values for salt rock samples are the highest with a mean of 4.620 W/m·K.Additionally,it is also found that the thermal conductivity of the same or similar lithologic types shows considerable differences,suggesting that thermal conductivity cannot be used for distinguishing the rock types.The thermal conductivity values of mudstone and sandstone generally increase with increasing burial depth and geological age of the formation,reflecting the effect of porosity of rocks on thermal conductivity.In general,the mineral composition,fabric and porosity of rocks are the main factors that affect the thermal conductivity.The research also reveals that the obvious contrast in thermal conductivity of coal and salt rock with other common sedimentary rocks can induce subsurface temperature anomalies in the overlying and underlying formations,which can modify the thermal evolution and maturity of the source rocks concerned.This finding is very important for oil and gas resources assessment and exploration and needs further study in detail.The results reported here are representative of the latest and most complete dataset of thermal conductivity of rocks in the Tarim basin,and will provide a solid foundation for geothermal studies in future.
基金NSERC (Natural Science and Engineering Research Council of Canada) for supporting this work
文摘Understanding and modelling the wettability of tight rocks is essential for designing fracturing and treatment fluids. In this paper, we measure and analyze spontaneous imbibition of water and oil into five twin core plugs drilled from the cores of a well drilled in the Montney Formation, an unconventional oil and gas play in the Western Canadian Sedimentary Basin. We characterize the samples by measuring the mineralogy using XRD（x-ray diffraction）, total organic carbon content, porosity, and permeability. Interestingly, the equilibrated water uptake of the five samples is similar, while, their oil uptake increases by increasing the core porosity and permeability. We define two wettability indices for the oil phase based on the slope and equilibrium values of water and oil imbibition curves. Both indices increase by increasing porosity and permeability, with the slope affinity index showing a stronger correlation. This observation suggests that part of the pore network has a stronger affinity to oil than to water. We also observe that the two indices decrease by increasing neutron porosity and gamma ray parameters measured by wireline logging tools. The samples with higher gamma ray and neutron porosity are expected to have greater clay content, and thus less effective porosity and permeability.
文摘The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin,Australia.This study involved combination methods such as petrophysical analysis,petrography and sedimentological studies,reservoir quality and fluid flow units from derivative parameters,and capillary pressure and wetting fluid saturation relationship.Textural and diagenetic features are affecting the reservoir quality.Cementation,compaction,and presence of clay minerals such as kaolinite are found to reduce the quality while dissolution and secondary porosity are noticed to improve it.It is believed that the Narimba Formation is a potential reservoir with a wide range of porosity and permeability.Porosity ranges from 3.1%to 25.4%with a mean of 15.84%,while permeability ranges between 0.01 mD and 510 mD,with a mean of 31.05 mD.Based on the heterogenous lithology,the formation has been categorized into five groups based on permeability variations.Group I showed an excellent to good quality reservoir with coarse grains.The impacts of both textural and diagenetic features improve the reservoir and producing higher reservoir quality index(RQI)and flow zone indicators(FZI)as well as mostly mega pores.The non-wetting fluid migration has the higher possibility to flow in the formation while displacement pressure recorded as zero.Group II showed a fair quality reservoir with lower petrophysical properties in macro pores.The irreducible water saturation is increasing while the textural and digenetic properties are still enhancing the reservoir quality.Group III reflects lower quality reservoir with mostly macro pores and higher displacement pressure.It may indicate smaller grain size and increasing amount of cement and clay minerals.Group IV,and V are interpreted as a poor-quality reservoir that has lower RQI and FZI.The textural and digenetic features are negatively affecting the reservoir and are leading to smaller pore size and pore throat radii(r35)values to be within the range of macro,meso-,micro-,and nano pores.The capillary displacement pressure curves of the three groups show increases reaching the maximum value of 400 psia in group V.Agreement with the classification of permeability,r35 values,and pore type can be used in identifying the quality of reservoir.
文摘The Eocene Sokor1 Formation is proven oil reservoir rocks in the Termit sub-basin. These sandstone intervals are deeply buried, highly heterogeneous in character and characterized by Low Contrast Low Resistivity (LCLR) log responses. Petrophysical and quantitative well-based rock physics interpretations were integrated for property estimations, fluid and lithology typing in reservoir characterization. Six (6) reservoir sandstone intervals were identified, delineated and correlated across five (5) wells. The estimated petrophysical properties showed that the Eocene Sokor1 sandstones have averagely good reservoir properties with sand_5 interval exhibiting exceptional reservoir properties. <i><span style="font-family:Verdana;">V</span><sub><span style="font-family:Verdana;">p</span></sub><span style="font-family:Verdana;">/V</span><sub><span style="font-family:Verdana;">s</span></sub></i><span style="font-family:Verdana;"> vs. AI and </span><i><span style="font-family:Verdana;">μρ</span></i><span style="font-family:Verdana;"> vs. </span><i><span style="font-family:Verdana;">λρ</span></i><span style="font-family:Verdana;"> elastic cross-plots color coded with reservoir properties (</span><i><span style="font-family:Verdana;">V</span><sub><span style="font-family:Verdana;">sh</span></sub></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;"><span style="white-space:nowrap;">Φ</span></span></i><span style="font-family:Verdana;">), show distinct and well separated data clusters signifying hydrocarbon bearing sandstones, brine sandstones and shales/mudstones in the 3D crossplot planes with varying seismic elastic property values in each well thereby, enhancing reservoir characterization and providing information’s about the burial history, reservoir quality and property distribution in the sub-basin. The analys</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> suggests that, although the reservoir interval has averagely good petrophysical properties in all wells, the seismic elastic crossplots show that these properties are much better distributed in wells 2 and 3 than in wells 4, 5 and 9. Therefore, sand_5 reservoir interval in wells 2 and 3 </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> likely to be more hydrocarbon bearing and productive than wells 4, 5 and 9 in the sub-basin.</span></span></span>
