By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using rese...By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using reservoir permeability and a synthesized index. The accuracy of this new method is verified by mercury-injection experiments. Considering the limited quantity of capillary pressure data, a new method is developed to extract the Swanson parameter from the NMR T2 distribution and estimate reservoir permeability. Integrating with NMR total porosity, reservoir capillary pressure curves can be constructed to evaluate reservoir pore structure in the intervals with NMR log data. An in-situ example of evaluating reservoir pore structure using the capillary pressure curves by this new method is presented. The result shows that it accurately detects the change in reservoir pore structure as a function of depth.展开更多
In recent years, nuclear magnetic resonance （NMR） has been increasingly used for fluid- typing in well-logging because of the improved generations of NMR logging tools. This paper first discusses the applicable cond...In recent years, nuclear magnetic resonance （NMR） has been increasingly used for fluid- typing in well-logging because of the improved generations of NMR logging tools. This paper first discusses the applicable conditions of two one-dimensional NMR methods： the dual TW method and dual TE method. Then, the two-dimensional （T2, D） and （T2, T1） NMR methods are introduced. These different typing methods for hydrocarbon are compared and analyzed by numerical simulation. The results show that the dual TW method is not suitable for identifying a macroporous water layer. The dual TE method is not suitable for typing gas and irreducible water. （T2, T1） method is more effective in typing a gas layer. In an oil-bearing layer of movable water containing big pores, （T2, T1） method can solve the misinterpretation problem in the dual TWmethod between a water layer with big pores and an oil layer. The （T2, T1） method can distinguish irreducible water from oil of a medium viscosity, and the viscosity range of oil becomes wide in contrast with that of the dual TW method. The （T2, D） method is more effective in typing oil and water layers. In a gas layer, when the SNR is higher than a threshold, the （T2, D） method can resolve the overlapping T2 signals of irreducible water and gas that occurs due to the use of the dual TE method. Twodimensional NMR for fluid-typing is an important development of well logging technology.展开更多
Nuclear magnetic resonance logging （NMR） is an open well logging method. Drilling mud resistivity, formation resistivity and sodium ions influence its radio frequency （RF） field strength and NMR logging signals. R...Nuclear magnetic resonance logging （NMR） is an open well logging method. Drilling mud resistivity, formation resistivity and sodium ions influence its radio frequency （RF） field strength and NMR logging signals. Research on these effects can provide an important basis for NMR logging data acquisition and interpretation. Three models, water-based drilling mud--water bearing formation, water- based drilling mud--oil bearing formation, oil-based drilling mud--water bearing formation, were studied by finite element method numerical simulation. The influences of drilling mud resistivity and formation resistivity on the NMR logging tool RF field and the influences of sodium ions on the NMR logging signals were simulated numerically. On the basis of analysis, RF field correction and sodium ion correction formulae were proposed and their application range was also discussed. The results indicate that when drilling mud resistivity and formation resistivity are 0.02 Ω·m and 0.2 Ω·m respectively, the attenuation index of centric NMR logging tool is 8.9% and 9.47% respectively. The RF field of an eccentric NMR logging tool is affected mainly by formation resistivity. When formation resistivity is 0.1 Ω·m, the attenuation index is 17.5%. For centric NMR logging tools, the signals coming from sodium ions can be up to 31.8% of total signal. Suggestions are proposed for further research into NMR logging tool correction method and response characteristics.展开更多
With complex lithology and reservoir types,as well as high concealment and heterogeneity,tight reservoirs in the Sichuan Basin involve significant uncertainties in gas-water relationship.Since NMR logging can effectiv...With complex lithology and reservoir types,as well as high concealment and heterogeneity,tight reservoirs in the Sichuan Basin involve significant uncertainties in gas-water relationship.Since NMR logging can effectively solve problems related to the multiple results of conventional logging operations,it can be deployed for accurate assessment of the properties of formation fluids.Accordingly,different NMR logging activation sets were assessed in accordance with the specific features of tight reservoirs in the basin.With consideration to NMR logging data obtained under different activation sets and testing data of wells,the optimal NMR logging activation set was identified.Moreover,with relaxation characteristics of rocks,gas and water as theoretical foundations,the T_(2) gas and water relaxation characteristics were reviewed to highlight the impacts of porosity,pore sizes,fluid properties and other factors of tight reservoirs on T_(2) horizontal relaxation distribution.According to the research results,D9TWE3 can be seen as the most suitable NMR logging activation set for tight reservoirs in the Sichuan Basin;reservoir tightness is the key influence factor for the distribution of gas/water relaxation in tight clastic reservoirs;generally,in tight sandstone reservoirs,natural gas shows a longer T_(2) relaxation time than water;in fracture-vug type carbonate reservoirs,the right peak of T_(2) distribution spectrum of gas layers is frontal,while the right peak in T_(2) distribution spectrum of water layers is backward.In conclusion,the standards for gas/water relaxation in tight sandstone and carbonate reservoirs in the Sichuan Basin can help effectively determine the physical properties of fluids in tight reservoirs with porosity of 4-10%.Such standards provide reliably technical supports for gas/water identification,reserves estimation and productivity construction in tight reservoirs of the Sichuan Basin.展开更多
This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR l...This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR logging data have some highly vital privileges over conventional ones.The measured porosity is independent from bearer pore fluid and is effective porosity not total.Moreover,the permeability achieved by exact measurement and calculation considering clay content and pore fluid type.Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir.Therefore,achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege.The input parameters of model were neutron porosity(NPHI),sonic transit time(DT),bulk density(RHOB)and electrical resistivity(RT).The outputs of model were also permeability and porosity values.The structure developed model was build and trained by using train data.Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data.Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir.Moreover,the considerable accuracy of newly ANN-Cuckoo method also demonstrated.This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.展开更多
Shale gas reservoirs have fine-grained textures and high organic contents,leading to complex pore structures.Therefore,accurate well-log derived pore size distributions are difficult to acquire for this unconventional...Shale gas reservoirs have fine-grained textures and high organic contents,leading to complex pore structures.Therefore,accurate well-log derived pore size distributions are difficult to acquire for this unconventional reservoir type,despite their importance.However,nuclear magnetic resonance(NMR)logging can in principle provide such information via hydrogen relaxation time measurements.Thus,in this paper,NMR response curves(of shale samples)were rigorously mathematically analyzed(with an Expectation Maximization algorithm)and categorized based on the NMR data and their geology,respectively.Thus the number of the NMR peaks,their relaxation times and amplitudes were analyzed to characterize pore size distributions and lithofacies.Seven pore size distribution classes were distinguished;these were verified independently with Pulsed-Neutron Spectrometry(PNS)well-log data.This study thus improves the interpretation of well log data in terms of pore structure and mineralogy of shale reservoirs,and consequently aids in the optimization of shale gas extraction from the subsurface.展开更多
Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or ...Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.展开更多
Porosity is a basic parameter for evaluating reservoir,and NMR logging is an effective method to obtain the porosity. However,we have often found that there exist significant differences between NMR po-rosities and fo...Porosity is a basic parameter for evaluating reservoir,and NMR logging is an effective method to obtain the porosity. However,we have often found that there exist significant differences between NMR po-rosities and formation core porosities in the complex reservoir. In this paper,we list the factors which affect the NMR porosity response in the complex reservoir,such as longitudinal relaxation time (T1),transverse relaxation time (T2),hydrogen index (HI) and borehole environment. We show how these factors affect the NMR porosity response and suggest methods to correct them. This should improve the accuracy of NMR logging porosity in complex reservoirs for the terrestrial formation.展开更多
Nuclear Magnetic Resonance mud logging technology (NMR mud logging) is a new mud logging technology. Mainly applies the CPMG(Carr-Purcell-Meiboom-Gill)pulse sequence to measure transverse relaxation time (T2) of the f...Nuclear Magnetic Resonance mud logging technology (NMR mud logging) is a new mud logging technology. Mainly applies the CPMG(Carr-Purcell-Meiboom-Gill)pulse sequence to measure transverse relaxation time (T2) of the fluid. NMR mud logging can measure drill cutting, core and sidewall core in the well site, also according to the experiment results, the sample type and size has little effect to analysis result. Through NMR logging, we can obtain several petrophysical parameters such as total porosity, effective porosity, permeability, oil saturation, water saturation, movable fluid saturation, movable oil saturation, movable water saturation, irreducible fluid saturation, irreducible oil saturation, irreducible water saturation, pore size and distribution in rock samples, etc. NMR mud logging has been used nearly 10 years in China, Sudan, Kazakhstan, etc. it plays an important role in the interpretation and evaluation of reservoir and its fluids.展开更多
The genesis mechanism of low-resistivity oil formation in the medium-deep Shahejie Formation of Bohai C Oilfield is unclear,and there is a lack of effective methods for identifying low-resistivity oil layers.This arti...The genesis mechanism of low-resistivity oil formation in the medium-deep Shahejie Formation of Bohai C Oilfield is unclear,and there is a lack of effective methods for identifying low-resistivity oil layers.This article conducts a comprehensive analysis based on core sample experiments,and research shows that the formation of low-resistivity oil layers in the oilfield is mainly caused by the superposition of three factors:1)microcapillary development,high irreducible water;2)additional conductive effect of clay;3)deep invasion of high salinity mud filtrate.The low-resistivity oil layer in this oilfield is mainly characterized by high mud content and strong additional conductivity of clay,and the complex pore throat structure leads to high irreducible water saturation,and the impact of saline mud intrusion,resulted in low-resistivity oil layers.The oil-field is mainly a lightweight oil layer with hydrophilic wettability,studying the response characteristics of oil and water layers through core nuclear magnetic resonance experiments,effectively identifying low-resistivity oil layers based on the correlation between resistivity and physical properties.展开更多
文摘By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using reservoir permeability and a synthesized index. The accuracy of this new method is verified by mercury-injection experiments. Considering the limited quantity of capillary pressure data, a new method is developed to extract the Swanson parameter from the NMR T2 distribution and estimate reservoir permeability. Integrating with NMR total porosity, reservoir capillary pressure curves can be constructed to evaluate reservoir pore structure in the intervals with NMR log data. An in-situ example of evaluating reservoir pore structure using the capillary pressure curves by this new method is presented. The result shows that it accurately detects the change in reservoir pore structure as a function of depth.
基金support from PetroChina Company Limited Innovation Foundation(Grant No.07-06D-01-04-01-07)State Key Laboratory of Petroleum Resource and Prospecting,China University of Petroleum(Beijing)(Grant No.PRPDX2008-02)
文摘In recent years, nuclear magnetic resonance （NMR） has been increasingly used for fluid- typing in well-logging because of the improved generations of NMR logging tools. This paper first discusses the applicable conditions of two one-dimensional NMR methods： the dual TW method and dual TE method. Then, the two-dimensional （T2, D） and （T2, T1） NMR methods are introduced. These different typing methods for hydrocarbon are compared and analyzed by numerical simulation. The results show that the dual TW method is not suitable for identifying a macroporous water layer. The dual TE method is not suitable for typing gas and irreducible water. （T2, T1） method is more effective in typing a gas layer. In an oil-bearing layer of movable water containing big pores, （T2, T1） method can solve the misinterpretation problem in the dual TWmethod between a water layer with big pores and an oil layer. The （T2, T1） method can distinguish irreducible water from oil of a medium viscosity, and the viscosity range of oil becomes wide in contrast with that of the dual TW method. The （T2, D） method is more effective in typing oil and water layers. In a gas layer, when the SNR is higher than a threshold, the （T2, D） method can resolve the overlapping T2 signals of irreducible water and gas that occurs due to the use of the dual TE method. Twodimensional NMR for fluid-typing is an important development of well logging technology.
基金supported by the National Natural Science Foundation of China (No.41074102)China International Science and Technology Cooperation (No.2009DFA61030)
文摘Nuclear magnetic resonance logging （NMR） is an open well logging method. Drilling mud resistivity, formation resistivity and sodium ions influence its radio frequency （RF） field strength and NMR logging signals. Research on these effects can provide an important basis for NMR logging data acquisition and interpretation. Three models, water-based drilling mud--water bearing formation, water- based drilling mud--oil bearing formation, oil-based drilling mud--water bearing formation, were studied by finite element method numerical simulation. The influences of drilling mud resistivity and formation resistivity on the NMR logging tool RF field and the influences of sodium ions on the NMR logging signals were simulated numerically. On the basis of analysis, RF field correction and sodium ion correction formulae were proposed and their application range was also discussed. The results indicate that when drilling mud resistivity and formation resistivity are 0.02 Ω·m and 0.2 Ω·m respectively, the attenuation index of centric NMR logging tool is 8.9% and 9.47% respectively. The RF field of an eccentric NMR logging tool is affected mainly by formation resistivity. When formation resistivity is 0.1 Ω·m, the attenuation index is 17.5%. For centric NMR logging tools, the signals coming from sodium ions can be up to 31.8% of total signal. Suggestions are proposed for further research into NMR logging tool correction method and response characteristics.
基金Project supported by the subject of“Logging identification and comprehensive evaluation techniques for clastic reservoirs in the Sichuan Basin”subordinate to the National Major Science&Technology Project“Development of large oil/gas fields and coalbed methane”(No.:2008ZX05002-004-003-001).
文摘With complex lithology and reservoir types,as well as high concealment and heterogeneity,tight reservoirs in the Sichuan Basin involve significant uncertainties in gas-water relationship.Since NMR logging can effectively solve problems related to the multiple results of conventional logging operations,it can be deployed for accurate assessment of the properties of formation fluids.Accordingly,different NMR logging activation sets were assessed in accordance with the specific features of tight reservoirs in the basin.With consideration to NMR logging data obtained under different activation sets and testing data of wells,the optimal NMR logging activation set was identified.Moreover,with relaxation characteristics of rocks,gas and water as theoretical foundations,the T_(2) gas and water relaxation characteristics were reviewed to highlight the impacts of porosity,pore sizes,fluid properties and other factors of tight reservoirs on T_(2) horizontal relaxation distribution.According to the research results,D9TWE3 can be seen as the most suitable NMR logging activation set for tight reservoirs in the Sichuan Basin;reservoir tightness is the key influence factor for the distribution of gas/water relaxation in tight clastic reservoirs;generally,in tight sandstone reservoirs,natural gas shows a longer T_(2) relaxation time than water;in fracture-vug type carbonate reservoirs,the right peak of T_(2) distribution spectrum of gas layers is frontal,while the right peak in T_(2) distribution spectrum of water layers is backward.In conclusion,the standards for gas/water relaxation in tight sandstone and carbonate reservoirs in the Sichuan Basin can help effectively determine the physical properties of fluids in tight reservoirs with porosity of 4-10%.Such standards provide reliably technical supports for gas/water identification,reserves estimation and productivity construction in tight reservoirs of the Sichuan Basin.
文摘This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR logging data have some highly vital privileges over conventional ones.The measured porosity is independent from bearer pore fluid and is effective porosity not total.Moreover,the permeability achieved by exact measurement and calculation considering clay content and pore fluid type.Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir.Therefore,achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege.The input parameters of model were neutron porosity(NPHI),sonic transit time(DT),bulk density(RHOB)and electrical resistivity(RT).The outputs of model were also permeability and porosity values.The structure developed model was build and trained by using train data.Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data.Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir.Moreover,the considerable accuracy of newly ANN-Cuckoo method also demonstrated.This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.
基金National Natural Science Foundation of China(41902145)Natural Science Basic Research Plan in Shaanxi Province of China(2020JQ-594)+2 种基金Young Talent fund of University Association for Science and Technology in Shaanxi,China(20180701)National and Local Joint Engineering Research Center for Carbon Capture Utilization and Sequestration at Northwest University in China.The measurements were performed using the mCT system of the National Geosequestration Laboratory(NGL)of Australia.Funding for the facilities was provided by the Australian Federal Governmentsupported by the Pawsey Supercomputing Centre,who provided the Avizo 9.5 image processing software and workstation,with funding from the Australian Government and the Government of Western Australia.
文摘Shale gas reservoirs have fine-grained textures and high organic contents,leading to complex pore structures.Therefore,accurate well-log derived pore size distributions are difficult to acquire for this unconventional reservoir type,despite their importance.However,nuclear magnetic resonance(NMR)logging can in principle provide such information via hydrogen relaxation time measurements.Thus,in this paper,NMR response curves(of shale samples)were rigorously mathematically analyzed(with an Expectation Maximization algorithm)and categorized based on the NMR data and their geology,respectively.Thus the number of the NMR peaks,their relaxation times and amplitudes were analyzed to characterize pore size distributions and lithofacies.Seven pore size distribution classes were distinguished;these were verified independently with Pulsed-Neutron Spectrometry(PNS)well-log data.This study thus improves the interpretation of well log data in terms of pore structure and mineralogy of shale reservoirs,and consequently aids in the optimization of shale gas extraction from the subsurface.
文摘Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.
基金Supported by the National Natural Science Foundation of China (Grant Nos.90510004 and 40674075)the Applied Basic Project of China National Petroleum Corporation (Grant No.06A30202)
文摘Porosity is a basic parameter for evaluating reservoir,and NMR logging is an effective method to obtain the porosity. However,we have often found that there exist significant differences between NMR po-rosities and formation core porosities in the complex reservoir. In this paper,we list the factors which affect the NMR porosity response in the complex reservoir,such as longitudinal relaxation time (T1),transverse relaxation time (T2),hydrogen index (HI) and borehole environment. We show how these factors affect the NMR porosity response and suggest methods to correct them. This should improve the accuracy of NMR logging porosity in complex reservoirs for the terrestrial formation.
文摘Nuclear Magnetic Resonance mud logging technology (NMR mud logging) is a new mud logging technology. Mainly applies the CPMG(Carr-Purcell-Meiboom-Gill)pulse sequence to measure transverse relaxation time (T2) of the fluid. NMR mud logging can measure drill cutting, core and sidewall core in the well site, also according to the experiment results, the sample type and size has little effect to analysis result. Through NMR logging, we can obtain several petrophysical parameters such as total porosity, effective porosity, permeability, oil saturation, water saturation, movable fluid saturation, movable oil saturation, movable water saturation, irreducible fluid saturation, irreducible oil saturation, irreducible water saturation, pore size and distribution in rock samples, etc. NMR mud logging has been used nearly 10 years in China, Sudan, Kazakhstan, etc. it plays an important role in the interpretation and evaluation of reservoir and its fluids.
文摘The genesis mechanism of low-resistivity oil formation in the medium-deep Shahejie Formation of Bohai C Oilfield is unclear,and there is a lack of effective methods for identifying low-resistivity oil layers.This article conducts a comprehensive analysis based on core sample experiments,and research shows that the formation of low-resistivity oil layers in the oilfield is mainly caused by the superposition of three factors:1)microcapillary development,high irreducible water;2)additional conductive effect of clay;3)deep invasion of high salinity mud filtrate.The low-resistivity oil layer in this oilfield is mainly characterized by high mud content and strong additional conductivity of clay,and the complex pore throat structure leads to high irreducible water saturation,and the impact of saline mud intrusion,resulted in low-resistivity oil layers.The oil-field is mainly a lightweight oil layer with hydrophilic wettability,studying the response characteristics of oil and water layers through core nuclear magnetic resonance experiments,effectively identifying low-resistivity oil layers based on the correlation between resistivity and physical properties.
