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
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
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
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
We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to...We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.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
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
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
Drilling optimization requires accurate drill bit rate-of-penetration(ROP)predictions.ROP decreases drilling time and costs and increases rig productivity.This study employs random forest(RF),gradient boosting modelin...Drilling optimization requires accurate drill bit rate-of-penetration(ROP)predictions.ROP decreases drilling time and costs and increases rig productivity.This study employs random forest(RF),gradient boosting modeling(GBM),extreme gradient boosting(XGBoost),and adaptive boosting(Adaboost)models to generate ROP pre-dictions.The models use well data from a 3200-m segment across the stratigraphic column(Dibdibba to Zubair formations)of the large West Qurna oil field in Southern Iraq,penetrating 19 formations and four oil reservoirs.The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies.The ROP predictive models were developed using 14 operational parameters:TVD,weight on bit(WOB),torque,effective circulating density(ECD),drilling rotation per minute(RPM),flow rate,standpipe pressure(SPP),bit size,total RPM,D exponent,gamma ray(GR),density,neutron,caliper,and discrete lithology distribution.Training and validation of the ROP models involves data compiled from three development wells.Applying Random subsampling,the compiled dataset was split into 85%for training and 15%for validation and testing.The test subgroup’s measured and predicted ROP mismatch was assessed using root mean square error(RMSE)and coefficient of correlation(R^(2)).The RF,GBM,and XGBoost models provide ROP predictions versus depth with low errors.Models with cross-validation that integrate data from three wells deliver more accurate ROP pre-dictions than datasets from single well.The input variables’influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.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
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
Global warming has greatly threatened the human living environment and carbon capture and storage(CCS)technology is recognized as a promising way to reduce carbon emissions.Mineral storage is considered a reliable opt...Global warming has greatly threatened the human living environment and carbon capture and storage(CCS)technology is recognized as a promising way to reduce carbon emissions.Mineral storage is considered a reliable option for long-term carbon storage.Basalt rich in alkaline earth elements facilitates rapid and permanent CO_(2) fixation as carbonates.However,the complex CO_(2)-fluid-basalt interaction poses challenges for assessing carbon storage potential.Under different reaction conditions,the carbonation products and carbonation rates vary.Carbon mineralization reactions also induce petrophysical and mechanical responses,which have potential risks for the long-term injectivity and the carbon storage safety in basalt reservoirs.In this paper,recent advances in carbon mineralization storage in basalt based on laboratory research are comprehensively reviewed.The assessment methods for carbon storage potential are introduced and the carbon trapping mechanisms are investigated with the identification of the controlling factors.Changes in pore structure,permeability and mechanical properties in both static reactions and reactive percolation experiments are also discussed.This study could provide insight into challenges as well as perspectives for future research.展开更多
Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation ...Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.展开更多
The Zeit sand reservoir is one of the most prolific formations at Northwestern side of the Gulf of Suez.In this research we will try to coordinate between electrical,petrophysical properties,depositional environment a...The Zeit sand reservoir is one of the most prolific formations at Northwestern side of the Gulf of Suez.In this research we will try to coordinate between electrical,petrophysical properties,depositional environment and facies discrimination in order to evaluate the hydrocarbon potentiality of studied Zeit Formation.The statistical parameters for potassium(K),thorium(Th)and Th/U ratio contents have a general increase towards northwestern parts,whereas uranium(U)content has a general increase towards southeastern parts.The sandstone facies is distinguished from the other facies by its thorium content>4 ppm.U has high carbonate content(U≥1 ppm).Rocks'electrical properties vary greatly depending on a number of factors.Electrical measurements were taken at frequencies range of(5×10^(-4)Hz-100 kHz)for fully saturated samples(clayey sandstone)with NaCl(20 gm/L).As salinity,clay content,and frequency increase,consequently does the electrical properties.The continental condition are present in northwestern part(back-sea)which is distinguished by high K percent,high Th,high Th/U ratio,and low U contents.Low K,Th,and Th/U ratio contents,with high U contents,characterize the marine depositional environment that existed around the east and southeastern parts(foresea coincide with the dipping of strata).Furthermore,the studied Zeit Formation has good petrophysical properties that coincide with marine conditions.The middle and eastern parts(around ISS-94 and CSS-288)is a good reservoir(porosity 36%-39%,shale content<15%,hydrocarbon saturation 71%-92%,and net pay thickness 17-63 feet).展开更多
The main objective is to optimize the development of shale gas-rich areas by predicting seismic sweet spot parameters in shale reservoirs. We systematically assessed the fracture development, fracture gas content, and...The main objective is to optimize the development of shale gas-rich areas by predicting seismic sweet spot parameters in shale reservoirs. We systematically assessed the fracture development, fracture gas content, and rock brittleness in fractured gas-bearing shale reservoirs. To better characterize gas-bearing shale reservoirs with tilted fractures, we optimized the petrophysical modeling based on the equivalent medium theory. Based on the advantages of shale petrophysical modeling, we not only considered the brittle mineral fraction but also the combined effect of shale porosity, gas saturation, and total organic carbon(TOC) when optimizing the brittleness index. Due to fractures generally functioning as essential channels for fluid storage and movement, fracture density and fracture fluid identification factors are critical geophysical parameters for fractured reservoir prediction. We defined a new fracture gas indication factor(GFI) to detect fracture-effective gas content. A new linear PP-wave reflection coefficient equation for a tilted transversely isotropic(TTI) medium was rederived, realizing the direct prediction of anisotropic fracture parameters and the isotropic elasticity parameters from offset vector tile(OVT)-domain seismic data. Synthetic seismic data experiments demonstrated that the inversion algorithm based on the L_P quasinorm sparsity constraint and the split-component inversion strategy exhibits high stability and noise resistance. Finally, we applied our new prediction method to evaluate fractured gas-bearing shale reservoirs in the Sichuan Basin of China, demonstrating its effectiveness.展开更多
The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,v...The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.展开更多
The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in th...The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.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.
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.
基金financially supported by the Russian federal research project No.FWZZ-2022-0026“Innovative aspects of electro-dynamics in problems of exploration and oilfield geophysics”.
文摘We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.
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Drilling optimization requires accurate drill bit rate-of-penetration(ROP)predictions.ROP decreases drilling time and costs and increases rig productivity.This study employs random forest(RF),gradient boosting modeling(GBM),extreme gradient boosting(XGBoost),and adaptive boosting(Adaboost)models to generate ROP pre-dictions.The models use well data from a 3200-m segment across the stratigraphic column(Dibdibba to Zubair formations)of the large West Qurna oil field in Southern Iraq,penetrating 19 formations and four oil reservoirs.The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies.The ROP predictive models were developed using 14 operational parameters:TVD,weight on bit(WOB),torque,effective circulating density(ECD),drilling rotation per minute(RPM),flow rate,standpipe pressure(SPP),bit size,total RPM,D exponent,gamma ray(GR),density,neutron,caliper,and discrete lithology distribution.Training and validation of the ROP models involves data compiled from three development wells.Applying Random subsampling,the compiled dataset was split into 85%for training and 15%for validation and testing.The test subgroup’s measured and predicted ROP mismatch was assessed using root mean square error(RMSE)and coefficient of correlation(R^(2)).The RF,GBM,and XGBoost models provide ROP predictions versus depth with low errors.Models with cross-validation that integrate data from three wells deliver more accurate ROP pre-dictions than datasets from single well.The input variables’influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.
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.
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.
基金funding support from the National Key R&D Program of China(Grant No.2022YFE0115800)the Creative Groups of Natural Science Foundation of Hubei Province(Grant No.2021CFA030)Shanxi Provincial Key Research and Development Project(Grant No.202102090301009).
文摘Global warming has greatly threatened the human living environment and carbon capture and storage(CCS)technology is recognized as a promising way to reduce carbon emissions.Mineral storage is considered a reliable option for long-term carbon storage.Basalt rich in alkaline earth elements facilitates rapid and permanent CO_(2) fixation as carbonates.However,the complex CO_(2)-fluid-basalt interaction poses challenges for assessing carbon storage potential.Under different reaction conditions,the carbonation products and carbonation rates vary.Carbon mineralization reactions also induce petrophysical and mechanical responses,which have potential risks for the long-term injectivity and the carbon storage safety in basalt reservoirs.In this paper,recent advances in carbon mineralization storage in basalt based on laboratory research are comprehensively reviewed.The assessment methods for carbon storage potential are introduced and the carbon trapping mechanisms are investigated with the identification of the controlling factors.Changes in pore structure,permeability and mechanical properties in both static reactions and reactive percolation experiments are also discussed.This study could provide insight into challenges as well as perspectives for future research.
文摘Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.
文摘The Zeit sand reservoir is one of the most prolific formations at Northwestern side of the Gulf of Suez.In this research we will try to coordinate between electrical,petrophysical properties,depositional environment and facies discrimination in order to evaluate the hydrocarbon potentiality of studied Zeit Formation.The statistical parameters for potassium(K),thorium(Th)and Th/U ratio contents have a general increase towards northwestern parts,whereas uranium(U)content has a general increase towards southeastern parts.The sandstone facies is distinguished from the other facies by its thorium content>4 ppm.U has high carbonate content(U≥1 ppm).Rocks'electrical properties vary greatly depending on a number of factors.Electrical measurements were taken at frequencies range of(5×10^(-4)Hz-100 kHz)for fully saturated samples(clayey sandstone)with NaCl(20 gm/L).As salinity,clay content,and frequency increase,consequently does the electrical properties.The continental condition are present in northwestern part(back-sea)which is distinguished by high K percent,high Th,high Th/U ratio,and low U contents.Low K,Th,and Th/U ratio contents,with high U contents,characterize the marine depositional environment that existed around the east and southeastern parts(foresea coincide with the dipping of strata).Furthermore,the studied Zeit Formation has good petrophysical properties that coincide with marine conditions.The middle and eastern parts(around ISS-94 and CSS-288)is a good reservoir(porosity 36%-39%,shale content<15%,hydrocarbon saturation 71%-92%,and net pay thickness 17-63 feet).
基金financially supported by the Sichuan Science and Technology Program (Grant No. 2023ZYD0158)the National Natural Science Foundation of China (Grant Nos. 42304147 and 42304076)。
文摘The main objective is to optimize the development of shale gas-rich areas by predicting seismic sweet spot parameters in shale reservoirs. We systematically assessed the fracture development, fracture gas content, and rock brittleness in fractured gas-bearing shale reservoirs. To better characterize gas-bearing shale reservoirs with tilted fractures, we optimized the petrophysical modeling based on the equivalent medium theory. Based on the advantages of shale petrophysical modeling, we not only considered the brittle mineral fraction but also the combined effect of shale porosity, gas saturation, and total organic carbon(TOC) when optimizing the brittleness index. Due to fractures generally functioning as essential channels for fluid storage and movement, fracture density and fracture fluid identification factors are critical geophysical parameters for fractured reservoir prediction. We defined a new fracture gas indication factor(GFI) to detect fracture-effective gas content. A new linear PP-wave reflection coefficient equation for a tilted transversely isotropic(TTI) medium was rederived, realizing the direct prediction of anisotropic fracture parameters and the isotropic elasticity parameters from offset vector tile(OVT)-domain seismic data. Synthetic seismic data experiments demonstrated that the inversion algorithm based on the L_P quasinorm sparsity constraint and the split-component inversion strategy exhibits high stability and noise resistance. Finally, we applied our new prediction method to evaluate fractured gas-bearing shale reservoirs in the Sichuan Basin of China, demonstrating its effectiveness.
文摘The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.
基金funded by the Science and Technology Project of Changzhou City(Grant No.CJ20210120)the Research Start-up Fund of Changzhou University(Grant No.ZMF21020056).
文摘The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.
