Sunaj Kumar, D. Mishra, S. Chatterjee, R. Tiwari, V. Avadhani
Since gas hydrates are unconventional reservoirs, they pose unique challenges for identification, characterization, quantification, and extraction. The conventional approach—elastic logs—can provide a better method for identification through attribute analysis. On the other hand, geomechanical studies for wellbore stability analysis pave the way for the effective exploitation of gas hydrates. It is crucial to predict elastic logs against gas-hydrate-bearing sediments, which requires an effective rock physics model. In the present work, a study pertaining to the National Gas Hydrate Program-02 (NGHP-02) campaign in the Krishna‐Godavari (KG) Offshore Basin, India, where gas hydrates are deposited primarily in two facies—a shale-dominated shallower one and a sand-dominated deeper one that has been identified by responses of conventional and spectroscopy logs—is discussed. It is commonly known that depositional heterogeneity impacts petrophysical and elastic properties. To address this issue, an innovative approach has been adopted to model compressional and shear log data using rock physics modeling of gas hydrate reservoirs based on the depositional type of gas hydrate. Guidance from the change of compressional velocity data from log and core with an increase of gas hydrate saturation shows gas hydrate deposition in the study area can be explained through a matrix/grain-supported model. The Jason grain-supported rock physics model appeared best suited among different available rock physics models, depending on the clay volume and porosity in our study area. Using input from a robust multimineral petrophysical evaluation and rock physics modeling, the finalized model is propagated to test wells for predicting compressional, shear, and density logs, with the predicted data validated by core-measured compressional and shear data. Model consistency is indicated by a high correlation from multiwell crossplots of modeled and recorded elastic logs (compressional and shear velocity) with acoustic impedance. The developed rock physics model better discriminates gas hydrate in the shaly sand layer and gas hydrate in the sand-dominated layer, calcite, and shale in the VpVs domain.
{"title":"Rock Physics Modeling of Gas Hydrate Reservoirs Through Integrated Core and Well-Log Data in NGHP-02 Area in KG Offshore Basin, India","authors":"Sunaj Kumar, D. Mishra, S. Chatterjee, R. Tiwari, V. Avadhani","doi":"10.30632/pjv63n2-2022a6","DOIUrl":"https://doi.org/10.30632/pjv63n2-2022a6","url":null,"abstract":"Since gas hydrates are unconventional reservoirs, they pose unique challenges for identification, characterization, quantification, and extraction. The conventional approach—elastic logs—can provide a better method for identification through attribute analysis. On the other hand, geomechanical studies for wellbore stability analysis pave the way for the effective exploitation of gas hydrates. It is crucial to predict elastic logs against gas-hydrate-bearing sediments, which requires an effective rock physics model. In the present work, a study pertaining to the National Gas Hydrate Program-02 (NGHP-02) campaign in the Krishna‐Godavari (KG) Offshore Basin, India, where gas hydrates are deposited primarily in two facies—a shale-dominated shallower one and a sand-dominated deeper one that has been identified by responses of conventional and spectroscopy logs—is discussed. It is commonly known that depositional heterogeneity impacts petrophysical and elastic properties. To address this issue, an innovative approach has been adopted to model compressional and shear log data using rock physics modeling of gas hydrate reservoirs based on the depositional type of gas hydrate. Guidance from the change of compressional velocity data from log and core with an increase of gas hydrate saturation shows gas hydrate deposition in the study area can be explained through a matrix/grain-supported model. The Jason grain-supported rock physics model appeared best suited among different available rock physics models, depending on the clay volume and porosity in our study area. Using input from a robust multimineral petrophysical evaluation and rock physics modeling, the finalized model is propagated to test wells for predicting compressional, shear, and density logs, with the predicted data validated by core-measured compressional and shear data. Model consistency is indicated by a high correlation from multiwell crossplots of modeled and recorded elastic logs (compressional and shear velocity) with acoustic impedance. The developed rock physics model better discriminates gas hydrate in the shaly sand layer and gas hydrate in the sand-dominated layer, calcite, and shale in the VpVs domain.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127662320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guangxu Zhou, Yan Ye, Jinzhi Zhu, Haoran Cheng, Hanxuan Song, Guanqun Wang, Wenjun Cai
Using core to conduct laboratory experiments is one of the important methods for wellbore stability research. However, as the objects of petroleum exploration gradually shift to areas with complex geological conditions, core acquisition is restricted by many problems, such as irregular shapes of cores obtained, low core acquisition rate, long construction time, and increasing operational cost. In recent years, exploration work in the southwest Tarim Basin has been further strengthened. Due to the complex strata and the frequent occurrence of complex well conditions during the drilling process, the problem of on-site coring has become more prominent, which seriously limits the analysis of rock properties. Therefore, scientific and effective cuttings analysis technology is urgently needed. This paper used digital core CT scanning technology to reconstruct the rock skeleton and its mineral facies using drill cuttings. The digital core was established, and the Poisson’s ratio and elastic modulus were calculated. The wellbore stability was analyzed, and the well sections, which were prone to wellbore instability, were determined. On this basis, KCl-polysulfonate drilling fluid was introduced. The influence of drilling fluid on rock strength was evaluated by digital core technology, and water immersion experiments were introduced to verify the experimental results. The results obtained by digital core CT scanning technology were compared with those obtained by triaxial compression experiments and well-logging data calculation, and the feasibility of digital core technology in the wellbore stability study and drilling fluid evaluation was verified.
{"title":"Application of Digital Core Technology in Wellbore Stability Research","authors":"Guangxu Zhou, Yan Ye, Jinzhi Zhu, Haoran Cheng, Hanxuan Song, Guanqun Wang, Wenjun Cai","doi":"10.30632/pjv63n2-2022a7","DOIUrl":"https://doi.org/10.30632/pjv63n2-2022a7","url":null,"abstract":"Using core to conduct laboratory experiments is one of the important methods for wellbore stability research. However, as the objects of petroleum exploration gradually shift to areas with complex geological conditions, core acquisition is restricted by many problems, such as irregular shapes of cores obtained, low core acquisition rate, long construction time, and increasing operational cost. In recent years, exploration work in the southwest Tarim Basin has been further strengthened. Due to the complex strata and the frequent occurrence of complex well conditions during the drilling process, the problem of on-site coring has become more prominent, which seriously limits the analysis of rock properties. Therefore, scientific and effective cuttings analysis technology is urgently needed. This paper used digital core CT scanning technology to reconstruct the rock skeleton and its mineral facies using drill cuttings. The digital core was established, and the Poisson’s ratio and elastic modulus were calculated. The wellbore stability was analyzed, and the well sections, which were prone to wellbore instability, were determined. On this basis, KCl-polysulfonate drilling fluid was introduced. The influence of drilling fluid on rock strength was evaluated by digital core technology, and water immersion experiments were introduced to verify the experimental results. The results obtained by digital core CT scanning technology were compared with those obtained by triaxial compression experiments and well-logging data calculation, and the feasibility of digital core technology in the wellbore stability study and drilling fluid evaluation was verified.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128898568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work establishes and verifies a laboratory method for accurate, noninvasive, and nondestructive in-situ measurement of filter-cake, or mudcake, properties using high-resolution X-ray microcomputed tomography (micro-CT). Accurate in-situ characterization of mudcake properties is of vital importance for understanding mudcake deposition during mud-filtrate invasion and, more broadly, the deposition of filter cake during industrial filtration processes. The developed laboratory method involves the injection of pressurized drilling mud into a borehole located at the center of a cylindrical rock core sample. Radial mud-filtrate invasion is induced by maintaining the outside of the core sample at a lower pressure. Mudcake is deposited on the borehole wall while mud filtrate flows into the surrounding pore space of the core sample. Simultaneously, the core sample is continuously scanned using high-resolution X-ray micro-CT, allowing for three-dimensional (3D) in-situ visualization and characterization of the deposited mudcake. Analysis of experimental results is aided by a new set of filtration equations that are generalized for use with either water-based mud or invert emulsion drilling fluids, such as oil- or synthetic oil-based mud. Each experiment produces a time-series data set consisting of injected mud volume and the corresponding in-situ average mudcake thickness, porosity, and permeability. Results are presented for experiments involving injection of water- and synthetic oil-based drilling mud. We observe that experimental measurements agree well with modeled results when mudcake is thin compared to the size of the borehole—a condition that would be satisfied under most normal field conditions. Initial results also suggest that the developed method may be capable of providing accurate in-situ 3D measurements of local filter-cake properties during compressible cake filtration, which has significant implications for a wide range of industrial applications.
{"title":"In-Situ Visualization and Characterization of Filter-Cake Deposition Using Time-Lapse Micro-CT Imaging","authors":"Colin Schroeder, C. Torres‐Verdín","doi":"10.30632/pjv63n2-2022a4","DOIUrl":"https://doi.org/10.30632/pjv63n2-2022a4","url":null,"abstract":"This work establishes and verifies a laboratory method for accurate, noninvasive, and nondestructive in-situ measurement of filter-cake, or mudcake, properties using high-resolution X-ray microcomputed tomography (micro-CT). Accurate in-situ characterization of mudcake properties is of vital importance for understanding mudcake deposition during mud-filtrate invasion and, more broadly, the deposition of filter cake during industrial filtration processes. The developed laboratory method involves the injection of pressurized drilling mud into a borehole located at the center of a cylindrical rock core sample. Radial mud-filtrate invasion is induced by maintaining the outside of the core sample at a lower pressure. Mudcake is deposited on the borehole wall while mud filtrate flows into the surrounding pore space of the core sample. Simultaneously, the core sample is continuously scanned using high-resolution X-ray micro-CT, allowing for three-dimensional (3D) in-situ visualization and characterization of the deposited mudcake. Analysis of experimental results is aided by a new set of filtration equations that are generalized for use with either water-based mud or invert emulsion drilling fluids, such as oil- or synthetic oil-based mud. Each experiment produces a time-series data set consisting of injected mud volume and the corresponding in-situ average mudcake thickness, porosity, and permeability. Results are presented for experiments involving injection of water- and synthetic oil-based drilling mud. We observe that experimental measurements agree well with modeled results when mudcake is thin compared to the size of the borehole—a condition that would be satisfied under most normal field conditions. Initial results also suggest that the developed method may be capable of providing accurate in-situ 3D measurements of local filter-cake properties during compressible cake filtration, which has significant implications for a wide range of industrial applications.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129823891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xun-cheng Song, Bernard Francis Sukari, Lei Wang, Zhiqiang Jiang, J. Cai, Yuqiang Xu, Hongqiang Huang
Accurate prediction of CH4 solubility in oil-based mud is significant for evaluating gas kick severity and implementing proper well control. The main factors affecting gas solubility in oil-based drilling fluid are base oil content, pressure, temperature, drilling fluid viscosity, and the components in natural gas. This work tests the complete series of solubility of high-purity CH4 in oil-based mud simulating downhole conditions on a 5,000-m-deep well relative to temperature, pressure, viscosity, and base oil content with temperature ranging from 40°C to 140°C and pressure ranging from 2 to 56 MPa using a PVT autoclave. Then, the effects of temperature, pressure, base oil content, and drilling fluid viscosity on the test target are analyzed using multiple experimental data processing methods. It is found that the variation of methane solubility with pressure and temperature are almost linearly under interested ranges. The effects of pressure (0.205 to ~0.294%/MPa) are much higher than that of temperature (–0.008 to –0.011%/°C) in oil-based drilling mud; the solubility of methane decreases as viscosity increases. Models for predicting methane solubility have been developed using screening procedures and statistics regression, which can be integrated into gas-liquid flow models to investigate flow behavior while gas kicking.
{"title":"Experimental Investigation on the Effect of Methane Solubility in Oil-Based Mud Under Downhole Conditions","authors":"Xun-cheng Song, Bernard Francis Sukari, Lei Wang, Zhiqiang Jiang, J. Cai, Yuqiang Xu, Hongqiang Huang","doi":"10.30632/pjv63n2-2022a5","DOIUrl":"https://doi.org/10.30632/pjv63n2-2022a5","url":null,"abstract":"Accurate prediction of CH4 solubility in oil-based mud is significant for evaluating gas kick severity and implementing proper well control. The main factors affecting gas solubility in oil-based drilling fluid are base oil content, pressure, temperature, drilling fluid viscosity, and the components in natural gas. This work tests the complete series of solubility of high-purity CH4 in oil-based mud simulating downhole conditions on a 5,000-m-deep well relative to temperature, pressure, viscosity, and base oil content with temperature ranging from 40°C to 140°C and pressure ranging from 2 to 56 MPa using a PVT autoclave. Then, the effects of temperature, pressure, base oil content, and drilling fluid viscosity on the test target are analyzed using multiple experimental data processing methods. It is found that the variation of methane solubility with pressure and temperature are almost linearly under interested ranges. The effects of pressure (0.205 to ~0.294%/MPa) are much higher than that of temperature (–0.008 to –0.011%/°C) in oil-based drilling mud; the solubility of methane decreases as viscosity increases. Models for predicting methane solubility have been developed using screening procedures and statistics regression, which can be integrated into gas-liquid flow models to investigate flow behavior while gas kicking.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116477520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Einar Madsen Storebø, Leonardo Teixeira Pinto Meireles, I. Fabricius
In this study, we investigate several published methods derived from Kozeny’s equation for permeability evaluation of the Lower Cretaceous marly chalk formations in the Danish North Sea. Using core measurements and well-logging data, we evaluate and compare controlling parameters for permeability calculation from spectral gamma ray, electrical resistivity, nuclear magnetic resonance, and sonic velocity logs. We provide a practical approach for modeling permeability at the well-log scale. We provide the obtained parameters for best fit according to each method for permeability modeling of clay-rich carbonate in the Danish North Sea Valdemar area.
{"title":"Permeability Modeling in Clay-Rich Carbonate Reservoir","authors":"Einar Madsen Storebø, Leonardo Teixeira Pinto Meireles, I. Fabricius","doi":"10.30632/pjv63n2-2022a2","DOIUrl":"https://doi.org/10.30632/pjv63n2-2022a2","url":null,"abstract":"In this study, we investigate several published methods derived from Kozeny’s equation for permeability evaluation of the Lower Cretaceous marly chalk formations in the Danish North Sea. Using core measurements and well-logging data, we evaluate and compare controlling parameters for permeability calculation from spectral gamma ray, electrical resistivity, nuclear magnetic resonance, and sonic velocity logs. We provide a practical approach for modeling permeability at the well-log scale. We provide the obtained parameters for best fit according to each method for permeability modeling of clay-rich carbonate in the Danish North Sea Valdemar area.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122536179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. A. Torres Caceres, K. Duffaut, A. Yazidi, F. Westad, Y. Johansen
During drilling and logging, depth alignment of well logs acquired in the same borehole section at different times is a vital preprocessing step before any petrophysical analysis. Depth alignment requires high precision as depth misalignment between different log curve measurements can substantially suppress possible correlations between formation properties, leading to imprecise interpretation or even misinterpretation. Standard depth alignment involves cross correlation, which typically requires user intervention for reliability. To improve the depth alignment process, we apply deep-learning techniques and propose a simple and practical implementation of a one-dimensional (1D) supervised convolutional neural network (1D CNN). We train seven CNN models using different log measurements, such as gamma ray, resistivity, P- and S-wave sonic, density, neutron, and photoelectric factor (PEF), to estimate depth mismatches between the corresponding raw logging-while-drilling (LWD) and electrical-wireline-logging (EWL) logs of each measurement type. Our deep-learning approach avoids manual feature extraction; hence, no high-level petrophysical knowledge is needed by our algorithms. We use log data from six wells from the Ivar Aasen Field in the Norwegian North Sea. Four of the six wells constitute the entire data set for training and model selection, in which we compare three search algorithms during the hyperparameter tuning. Only two wells have both LWD and EWL log suites. These wells are used for depth-shift inference. We focus on estimating bulk shifts, and we assume the existence of small pattern differences. We assess our results by visual inspection and quantitative metrics such as the Pearson correlation and Euclidean distance. We also compare the CNN depth shifts with depth shifts obtained using the classical cross-correlation method. The CNN performs well and is competitive with cross correlation. CNN performs better for some log types—resistivity, for instance—than others. Several factors influence our results, including the quality of the input data, borehole conditions, pattern differences between LWD and EWL, and significant stretch/squeeze effects. Differences between the mean Pearson correlation computed after CNN and the cross-correlation depth-matching process are of the order of 10–1 and 10–2. Our CNN approach is, therefore, a potential alternative to current depth-matching methods, which may reduce the amount of user intervention required from the petrophysicist.
{"title":"Automated Well-Log Depth Matching – 1D Convolutional Neural Networks Vs. Classic Cross Correlation","authors":"V. A. Torres Caceres, K. Duffaut, A. Yazidi, F. Westad, Y. Johansen","doi":"10.30632/pjv63n1-2022a2","DOIUrl":"https://doi.org/10.30632/pjv63n1-2022a2","url":null,"abstract":"During drilling and logging, depth alignment of well logs acquired in the same borehole section at different times is a vital preprocessing step before any petrophysical analysis. Depth alignment requires high precision as depth misalignment between different log curve measurements can substantially suppress possible correlations between formation properties, leading to imprecise interpretation or even misinterpretation. Standard depth alignment involves cross correlation, which typically requires user intervention for reliability. To improve the depth alignment process, we apply deep-learning techniques and propose a simple and practical implementation of a one-dimensional (1D) supervised convolutional neural network (1D CNN). We train seven CNN models using different log measurements, such as gamma ray, resistivity, P- and S-wave sonic, density, neutron, and photoelectric factor (PEF), to estimate depth mismatches between the corresponding raw logging-while-drilling (LWD) and electrical-wireline-logging (EWL) logs of each measurement type. Our deep-learning approach avoids manual feature extraction; hence, no high-level petrophysical knowledge is needed by our algorithms. We use log data from six wells from the Ivar Aasen Field in the Norwegian North Sea. Four of the six wells constitute the entire data set for training and model selection, in which we compare three search algorithms during the hyperparameter tuning. Only two wells have both LWD and EWL log suites. These wells are used for depth-shift inference. We focus on estimating bulk shifts, and we assume the existence of small pattern differences. We assess our results by visual inspection and quantitative metrics such as the Pearson correlation and Euclidean distance. We also compare the CNN depth shifts with depth shifts obtained using the classical cross-correlation method. The CNN performs well and is competitive with cross correlation. CNN performs better for some log types—resistivity, for instance—than others. Several factors influence our results, including the quality of the input data, borehole conditions, pattern differences between LWD and EWL, and significant stretch/squeeze effects. Differences between the mean Pearson correlation computed after CNN and the cross-correlation depth-matching process are of the order of 10–1 and 10–2. Our CNN approach is, therefore, a potential alternative to current depth-matching methods, which may reduce the amount of user intervention required from the petrophysicist.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"57 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116432078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the end of oil production, the need for safe and economically efficient plug and abandonment (P&A) operations increases. Due to the high costs related to traditional P&A processes, the possibility of shale bonding to the well casing, forming a natural barrier, is explored. Here, we present a unique laboratory experiment of creeping shale in a pressure cell, which is monitored during the creep and bonding processes using the ultrasound pulse-echo (PE) and pitch-catch (PC) measurement techniques. The combination of continuous PE and PC measurements in one direction, with discrete measurements in 5° intervals, and the logging of confining pressure, temperature, and permeability allows an improved understanding of the bonding processes and the interpretation of parameters derived from PE and PC measurements. We find that the initial creep of the shale occurs when the confining pressure is increased from 5 to 9 MPa, resulting in significant changes observed in the acoustic impedances derived from PE and PC measurements. However, as permeability is still high, we assume that channels are still open. With the increase in confining pressure to 13 and 14 MPa, respectively, we observe a decrease in permeability corresponding to an overall decrease in signal strength for the PC data of up to 25 dB and a decrease in the PE group delay parameter of up to 0.6 μs corresponding to a change in acoustic impedance from about 1 MRayl to about 3 to 3.5 MRayl. Large variations with direction can be observed. Combining the information of PE and PC gives a good impression of the bonding processes.
{"title":"Ultrasonic Logging of Creeping Shale","authors":"A. Diez, T. Johansen, I. Larsen","doi":"10.30632/pjv63n1-2022a4","DOIUrl":"https://doi.org/10.30632/pjv63n1-2022a4","url":null,"abstract":"With the end of oil production, the need for safe and economically efficient plug and abandonment (P&A) operations increases. Due to the high costs related to traditional P&A processes, the possibility of shale bonding to the well casing, forming a natural barrier, is explored. Here, we present a unique laboratory experiment of creeping shale in a pressure cell, which is monitored during the creep and bonding processes using the ultrasound pulse-echo (PE) and pitch-catch (PC) measurement techniques. The combination of continuous PE and PC measurements in one direction, with discrete measurements in 5° intervals, and the logging of confining pressure, temperature, and permeability allows an improved understanding of the bonding processes and the interpretation of parameters derived from PE and PC measurements. We find that the initial creep of the shale occurs when the confining pressure is increased from 5 to 9 MPa, resulting in significant changes observed in the acoustic impedances derived from PE and PC measurements. However, as permeability is still high, we assume that channels are still open. With the increase in confining pressure to 13 and 14 MPa, respectively, we observe a decrease in permeability corresponding to an overall decrease in signal strength for the PC data of up to 25 dB and a decrease in the PE group delay parameter of up to 0.6 μs corresponding to a change in acoustic impedance from about 1 MRayl to about 3 to 3.5 MRayl. Large variations with direction can be observed. Combining the information of PE and PC gives a good impression of the bonding processes.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129718713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Delaware Basin of the greater Permian Basin system has been the focus of continually increased attention in the exploration, appraisal, and development phases of unconventional oil and gas potential over the past decade. While the industry continues to drill horizontal wells for the exploitation of producible hydrocarbon, subsurface disciplines continue to investigate the rock and fluid properties of the stratigraphy through the application of various technological tools. In this study, we focus on five wells spatially covering Loving, Ward, and Reeves Counties in West Texas, where whole core samples were acquired and investigated to compare variations in the Bone Spring and Wolfcamp Formations across varying geological contexts. Samples taken from the whole conventional core were investigated in the laboratory setting, and a series of measurements were acquired on each sample. The laboratory measurements distinguished trends and changes in the rock property volumes (i.e., porosity, saturation, total organic carbon) over stratigraphic intervals. The utilization of nuclear magnetic resonance, as well as acquired geochemical data, allows an innovative approach and application of a correction factor to be applied to the saturation quantification. Integration of geological context with measured laboratory data constraints and petrophysical wireline-log-based interpretation links predictive trends from the defined rock and fluid property distributions and may aid in predicting hydrocarbon vs. water production.
{"title":"Investigating Delaware Basin Bone Spring and Wolfcamp Observations Through Core-Based Quantification: Case Study in the Integrated Workflow, Including Closed Retort Comparisons","authors":"S. Perry, J. Zumberge, K. Cheng","doi":"10.30632/pjv63n1-2022a6","DOIUrl":"https://doi.org/10.30632/pjv63n1-2022a6","url":null,"abstract":"The Delaware Basin of the greater Permian Basin system has been the focus of continually increased attention in the exploration, appraisal, and development phases of unconventional oil and gas potential over the past decade. While the industry continues to drill horizontal wells for the exploitation of producible hydrocarbon, subsurface disciplines continue to investigate the rock and fluid properties of the stratigraphy through the application of various technological tools. In this study, we focus on five wells spatially covering Loving, Ward, and Reeves Counties in West Texas, where whole core samples were acquired and investigated to compare variations in the Bone Spring and Wolfcamp Formations across varying geological contexts. Samples taken from the whole conventional core were investigated in the laboratory setting, and a series of measurements were acquired on each sample. The laboratory measurements distinguished trends and changes in the rock property volumes (i.e., porosity, saturation, total organic carbon) over stratigraphic intervals. The utilization of nuclear magnetic resonance, as well as acquired geochemical data, allows an innovative approach and application of a correction factor to be applied to the saturation quantification. Integration of geological context with measured laboratory data constraints and petrophysical wireline-log-based interpretation links predictive trends from the defined rock and fluid property distributions and may aid in predicting hydrocarbon vs. water production.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123625801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. A. Torres Caceres, K. Duffaut, F. Westad, A. Stovas, Y. Johansen, Arne Jenssen
The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effort to transform standard work procedures and workflows into more efficient practices and implementations using machine learning (ML) and automation. This will enable geoscientists to explore and exploit vast amounts of data quickly and efficiently. To address these current industry challenges, we propose a pilot well-log database in HDF5 (Hierarchical Data Format version 5) format that can be continuously extended if new data become available. It also provides versatility for data preparation for further analysis. We show an alternative way to store and use log files in a hierarchical structure that is easy to understand and handle by research institutes, companies, and academia. We also touch upon well-log depth matching, a long-standing industry challenge, to synchronize data from different logging passes to a single depth reference. Having a robust automated solution for depth matching is important to facilitate the use of all available data in a depth interval for analysis by ML. We propose an automatic well-log depth-matching workflow capable of handling multiple log types simultaneously and its integration with the database. The updated depth-matched logs are added to the database with their corresponding metadata, giving the geoscientist full control. We implemented two algorithms—classical cross correlation combined with a scaling factor to simulate stretch-squeeze effects and a constrained dynamic time warping (DTW). Our results indicate that the classical cross correlation outperforms the warping for both robustness and speed when the DTW is constrained to avoid excessive signal distortion and when the number of processed curves increases, respectively. Some limitations of our approach are related to large changes in the log patterns between the runs, as well as the assumption of negligible depth shift between log types within the same run. The cross correlation also allows a consistent application of depth matching to the metadata. This prototype workflow is tested using two wells from the Norwegian North Sea. We see the potential for extending this automatic database-processing workflow to give geoscientists access to all the data to improve interpretation.
{"title":"Automated Log Data Analytics Workflow – The Value of Data Access and Management to Reduced Turnaround Time for Log Analysis","authors":"V. A. Torres Caceres, K. Duffaut, F. Westad, A. Stovas, Y. Johansen, Arne Jenssen","doi":"10.30632/pjv63n1-2022a3","DOIUrl":"https://doi.org/10.30632/pjv63n1-2022a3","url":null,"abstract":"The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effort to transform standard work procedures and workflows into more efficient practices and implementations using machine learning (ML) and automation. This will enable geoscientists to explore and exploit vast amounts of data quickly and efficiently. To address these current industry challenges, we propose a pilot well-log database in HDF5 (Hierarchical Data Format version 5) format that can be continuously extended if new data become available. It also provides versatility for data preparation for further analysis. We show an alternative way to store and use log files in a hierarchical structure that is easy to understand and handle by research institutes, companies, and academia. We also touch upon well-log depth matching, a long-standing industry challenge, to synchronize data from different logging passes to a single depth reference. Having a robust automated solution for depth matching is important to facilitate the use of all available data in a depth interval for analysis by ML. We propose an automatic well-log depth-matching workflow capable of handling multiple log types simultaneously and its integration with the database. The updated depth-matched logs are added to the database with their corresponding metadata, giving the geoscientist full control. We implemented two algorithms—classical cross correlation combined with a scaling factor to simulate stretch-squeeze effects and a constrained dynamic time warping (DTW). Our results indicate that the classical cross correlation outperforms the warping for both robustness and speed when the DTW is constrained to avoid excessive signal distortion and when the number of processed curves increases, respectively. Some limitations of our approach are related to large changes in the log patterns between the runs, as well as the assumption of negligible depth shift between log types within the same run. The cross correlation also allows a consistent application of depth matching to the metadata. This prototype workflow is tested using two wells from the Norwegian North Sea. We see the potential for extending this automatic database-processing workflow to give geoscientists access to all the data to improve interpretation.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122052085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is currently no production logging tool that can analyze in-situ gas composition in wells that intersect more than one pay zone. In high-value gas wells, zonal isolation is useful for enhanced oil recovery (EOR) applications such as injection monitoring, pressure maintenance, and identification of layers that contain low-calorific-value gas. To unlock these key applications related to gas reservoir management, a new production logging tool was developed that introduces downhole gas composition analysis to the production logging tool string. The tool does not require a formation seal and so can log continuously or at a station. For gas field applications with pressures above 3,000 psi, the estimated mole fraction errors in continuous logging mode are ~2% for methane and ~1% for ethane, propane, nitrogen, and carbon dioxide. In formations with lower pressures, station logs can achieve mole fraction errors < 1% for all components.
{"title":"New Logging Tool for Enhanced Oil Recovery and Gas Storage Monitoring Applications","authors":"A. Andrews, Andrew J. Speck","doi":"10.30632/pjv63n1-2022a1","DOIUrl":"https://doi.org/10.30632/pjv63n1-2022a1","url":null,"abstract":"There is currently no production logging tool that can analyze in-situ gas composition in wells that intersect more than one pay zone. In high-value gas wells, zonal isolation is useful for enhanced oil recovery (EOR) applications such as injection monitoring, pressure maintenance, and identification of layers that contain low-calorific-value gas. To unlock these key applications related to gas reservoir management, a new production logging tool was developed that introduces downhole gas composition analysis to the production logging tool string. The tool does not require a formation seal and so can log continuously or at a station. For gas field applications with pressures above 3,000 psi, the estimated mole fraction errors in continuous logging mode are ~2% for methane and ~1% for ethane, propane, nitrogen, and carbon dioxide. In formations with lower pressures, station logs can achieve mole fraction errors < 1% for all components.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132006877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：