Yuejiao Liu, Haitao Wang, F. Lai, Ruyue Wang, Haijie Zhang, Xiaoshu Zhang, Fahui Ou
Conventional petrophysical experiments in deep shale gas reservoirs are characterized by difficult coring, high cost, and insufficient representative samples, so it is difficult to comprehensively investigate the key factors of Poisson’s ratio through petrophysical experiments. In this study, a multiscale and multicomponent three-dimensional (3D) digital core was constructed for the shale gas reservoir of Wufeng Formation-Longmaxi Formation in the Dazu area, Western Chongqing, China, to quantitatively simulate the influences of the changes of reservoir gas saturation, mineral composition, stratification, and fractures on Poisson’s ratio. The absolute errors between Poisson’s ratio measured by core experiments, Poisson’s ratio simulated by the multiscale and multicomponent digital core, and Poisson’s ratio calculated with the time differences of longitudinal and transverse waves were analyzed. The analysis results showed that Poisson’s ratio was sensitive to stratification dip angle and fracture dip angle. When the stratification dip angle or fracture dip angle was close to 45°, Poisson’s ratio reached its minimum value. Poisson’s ratio was more sensitive to the content of calcite than the contents of quartz, dolomite, and pyrite. The influence of gas saturation on Poisson’s ratio was the least. The average error between Poisson’s ratio measured by core experiments and Poisson’s ratio simulated by the multiscale and multicomponent digital core was 4.920%. The average error between Poisson’s ratio measured by core experiments and Poisson’s ratio calculated with the time differences of longitudinal and transverse waves was 10.968%.
{"title":"Analysis of Influencing Factors of Poisson’s Ratio in Deep Shale Gas Reservoir Based on Digital Core Simulation","authors":"Yuejiao Liu, Haitao Wang, F. Lai, Ruyue Wang, Haijie Zhang, Xiaoshu Zhang, Fahui Ou","doi":"10.30632/pjv64n1-2023a5","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a5","url":null,"abstract":"Conventional petrophysical experiments in deep shale gas reservoirs are characterized by difficult coring, high cost, and insufficient representative samples, so it is difficult to comprehensively investigate the key factors of Poisson’s ratio through petrophysical experiments. In this study, a multiscale and multicomponent three-dimensional (3D) digital core was constructed for the shale gas reservoir of Wufeng Formation-Longmaxi Formation in the Dazu area, Western Chongqing, China, to quantitatively simulate the influences of the changes of reservoir gas saturation, mineral composition, stratification, and fractures on Poisson’s ratio. The absolute errors between Poisson’s ratio measured by core experiments, Poisson’s ratio simulated by the multiscale and multicomponent digital core, and Poisson’s ratio calculated with the time differences of longitudinal and transverse waves were analyzed. The analysis results showed that Poisson’s ratio was sensitive to stratification dip angle and fracture dip angle. When the stratification dip angle or fracture dip angle was close to 45°, Poisson’s ratio reached its minimum value. Poisson’s ratio was more sensitive to the content of calcite than the contents of quartz, dolomite, and pyrite. The influence of gas saturation on Poisson’s ratio was the least. The average error between Poisson’s ratio measured by core experiments and Poisson’s ratio simulated by the multiscale and multicomponent digital core was 4.920%. The average error between Poisson’s ratio measured by core experiments and Poisson’s ratio calculated with the time differences of longitudinal and transverse waves was 10.968%.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718726","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}
Imaging logging is a method of imaging the physical parameters of the borehole wall or the objects around the borehole according to the observation of the geophysical field in the borehole. Imaging logging data can determine the dip angle and structural characteristics of the formation and observe the geometry and development degree of fractures. The performance of existing target segmentation networks relies on large volumes of data. However, logging images are expensive to acquire, so how to effectively extract fractures from small samples of logging images is an urgent problem to be solved. Therefore, we developed a dual encoder-decoder structure using the Swin Transformer, which uses the self-attention mechanism of a hierarchical Vision Transformer with shifted window to model the remote context information. It can overcome the limitations of most convolutional neural network-based methods that cannot establish long-term dependencies and global contextual connections in convolutional operations. In addition, the shifted window mechanism substantially improves the computational efficiency of the model, and the hierarchical structure allows flexibility in modeling at different scales. At the same time, skip connections are established between adjacent layers of the structure, and the higher-level feature maps are stitched with the lower-level feature maps in channel dimensions, which can obtain more high-resolution detail information of fractures, and thus improve the segmentation accuracy. The experimental results show that the performance is better than the mainstream segmentation networks under small training sets of logging images. The effectiveness of our method reveals that it is practical in fracture extraction of logging images.
{"title":"Fracture Extraction From Logging Image Using a Dual Encoder-Decoder Architecture With Swin Transformer","authors":"","doi":"10.30632/pjv64n1-2023a3","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a3","url":null,"abstract":"Imaging logging is a method of imaging the physical parameters of the borehole wall or the objects around the borehole according to the observation of the geophysical field in the borehole. Imaging logging data can determine the dip angle and structural characteristics of the formation and observe the geometry and development degree of fractures. The performance of existing target segmentation networks relies on large volumes of data. However, logging images are expensive to acquire, so how to effectively extract fractures from small samples of logging images is an urgent problem to be solved. Therefore, we developed a dual encoder-decoder structure using the Swin Transformer, which uses the self-attention mechanism of a hierarchical Vision Transformer with shifted window to model the remote context information. It can overcome the limitations of most convolutional neural network-based methods that cannot establish long-term dependencies and global contextual connections in convolutional operations. In addition, the shifted window mechanism substantially improves the computational efficiency of the model, and the hierarchical structure allows flexibility in modeling at different scales. At the same time, skip connections are established between adjacent layers of the structure, and the higher-level feature maps are stitched with the lower-level feature maps in channel dimensions, which can obtain more high-resolution detail information of fractures, and thus improve the segmentation accuracy. The experimental results show that the performance is better than the mainstream segmentation networks under small training sets of logging images. The effectiveness of our method reveals that it is practical in fracture extraction of logging images.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121692890","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}
Due to the complexity of lithologies and pore types, the permeability calculation of complex carbonate reservoirs has always been a difficult problem. To accurately calculate the permeability of complex carbonate reservoirs, a data mining technique is introduced. The technical process of data mining is established and divided into seven steps: data warehousing, data preprocessing, classification of reservoir types, selection of sensitive parameters, establishment of the classification model, evaluation of classification model, and application of classification model. The data-driven method can find effective knowledge that conventional reservoir evaluation methods cannot recognize and that are still contained in oil and gas data. Since the data-driven method may acquire a large amount of invalid knowledge while obtaining effective knowledge, the domain knowledge needs to be introduced to participate in the data mining process. The domain-knowledge-driven method can extract the most valuable and effective information from oil and gas data. The combination of data-driven and domain knowledge-driven methods is possible to avoid subdividing lithologies and pore types of complex carbonate reservoirs. As a result, the permeability of complex carbonate reservoirs can be accurately calculated based on the combination of data-driven and domain-knowledge-driven methods. Compared with the permeability calculation result by the previous method, the accuracy of the permeability calculation result by the data mining technique is improved by 18.39%. The combination of data-driven and domain-knowledge-driven methods can solve the difficult problem that traditional reservoir evaluation methods cannot overcome. Additionally, they can also provide new theories and techniques for reservoir evaluation. The permeability calculation result proves the feasibility and correctness of the method.
{"title":"Permeability Calculation of Complex Carbonate Reservoirs Based on Data Mining Techniques","authors":"Xiongyan Li","doi":"10.30632/pjv64n1-2023a7","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a7","url":null,"abstract":"Due to the complexity of lithologies and pore types, the permeability calculation of complex carbonate reservoirs has always been a difficult problem. To accurately calculate the permeability of complex carbonate reservoirs, a data mining technique is introduced. The technical process of data mining is established and divided into seven steps: data warehousing, data preprocessing, classification of reservoir types, selection of sensitive parameters, establishment of the classification model, evaluation of classification model, and application of classification model. The data-driven method can find effective knowledge that conventional reservoir evaluation methods cannot recognize and that are still contained in oil and gas data. Since the data-driven method may acquire a large amount of invalid knowledge while obtaining effective knowledge, the domain knowledge needs to be introduced to participate in the data mining process. The domain-knowledge-driven method can extract the most valuable and effective information from oil and gas data. The combination of data-driven and domain knowledge-driven methods is possible to avoid subdividing lithologies and pore types of complex carbonate reservoirs. As a result, the permeability of complex carbonate reservoirs can be accurately calculated based on the combination of data-driven and domain-knowledge-driven methods. Compared with the permeability calculation result by the previous method, the accuracy of the permeability calculation result by the data mining technique is improved by 18.39%. The combination of data-driven and domain-knowledge-driven methods can solve the difficult problem that traditional reservoir evaluation methods cannot overcome. Additionally, they can also provide new theories and techniques for reservoir evaluation. The permeability calculation result proves the feasibility and correctness of the method.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130142467","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}
N. Jahani, S. Alyaev, J. Ambía, K. Fossum, E. Suter, C. Torres‐Verdín
The cost of drilling wells on the Norwegian Continental Shelf is exceptionally high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted by repeated formation evaluation based on the interpretation of well logs while drilling. Thus, reliable, computationally efficient, and robust workflows that can interpret well logs and capture uncertainties in real time are necessary for successful well placement. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Levenberg-Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model parameters and their uncertainties. This paper demonstrates joint estimation of layer-by-layer water saturation, porosity, and layer-boundary locations and inference of layers’ resistivities and densities. The parameters are estimated by minimizing the statistical misfit between the simulated and the observed measurements for several logs on different scales simultaneously (i.e., shallow-sensing nuclear density and shallow to extra-deep electromagnetic (EM) logs). Numerical experiments performed on a synthetic example verified that the iterative ensemble-based method could estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of the time compared to classical Monte Carlo methods. Extra-deep EM measurements provide the best reliable information for geosteering, and we show that they can be interpreted within the proposed workflow. However, we also observe that the parameter uncertainties noticeably decrease when deep-sensing EM logs are combined with shallow-sensing nuclear density logs. Importantly, the estimation quality increases not only in the proximity of the shallow tool but also extends to the look ahead of the extra-deep EM capabilities. We specifically quantify how shallow data can lead to significant uncertainty reduction of the boundary positions ahead of the bit, which is crucial for geosteering decisions and reservoir mapping.
{"title":"Enhancing the Detectability of Deep-Sensing Borehole Electromagnetic Instruments by Joint Inversion of Multiple Logs Within a Probabilistic Geosteering Workflow","authors":"N. Jahani, S. Alyaev, J. Ambía, K. Fossum, E. Suter, C. Torres‐Verdín","doi":"10.30632/pjv64n1-2023a6","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a6","url":null,"abstract":"The cost of drilling wells on the Norwegian Continental Shelf is exceptionally high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted by repeated formation evaluation based on the interpretation of well logs while drilling. Thus, reliable, computationally efficient, and robust workflows that can interpret well logs and capture uncertainties in real time are necessary for successful well placement. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Levenberg-Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model parameters and their uncertainties. This paper demonstrates joint estimation of layer-by-layer water saturation, porosity, and layer-boundary locations and inference of layers’ resistivities and densities. The parameters are estimated by minimizing the statistical misfit between the simulated and the observed measurements for several logs on different scales simultaneously (i.e., shallow-sensing nuclear density and shallow to extra-deep electromagnetic (EM) logs). Numerical experiments performed on a synthetic example verified that the iterative ensemble-based method could estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of the time compared to classical Monte Carlo methods. Extra-deep EM measurements provide the best reliable information for geosteering, and we show that they can be interpreted within the proposed workflow. However, we also observe that the parameter uncertainties noticeably decrease when deep-sensing EM logs are combined with shallow-sensing nuclear density logs. Importantly, the estimation quality increases not only in the proximity of the shallow tool but also extends to the look ahead of the extra-deep EM capabilities. We specifically quantify how shallow data can lead to significant uncertainty reduction of the boundary positions ahead of the bit, which is crucial for geosteering decisions and reservoir mapping.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"45 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114456998","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 advancement of conventional water displacement technology, cold damage may affect the vicinity of high pour point oil wells to a certain extent. Thus, the optimization of thermal recovery parameters of high pour point oil is of great significance to the oil field. In this study, a comprehensive model of the temperature distribution of the wellbore fluid was constructed, and the function of f(tD) was introduced to participate in the calculation of the thermal resistance of the formation. The temperature distribution along the wellbore was calculated and simulated under different conditions. The results show that the surface water injection temperature of 60.8℃ could ensure the desired water temperature reaching the target layer, and the injection temperature of 60.4°C could meet the needs of efficient development after considering the design of the 750-m insulated tubing. The final thermal recovery parameter optimization system could provide a theoretical reference for the thermal recovery design of the same type of reservoir.
{"title":"An Algorithm to Optimize Water Injection Temperature for Thermal Recovery of High Pour Point Oil","authors":"YU Peng, Shengqiang Zhang","doi":"10.30632/pjv64n1-2023a8","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a8","url":null,"abstract":"With the advancement of conventional water displacement technology, cold damage may affect the vicinity of high pour point oil wells to a certain extent. Thus, the optimization of thermal recovery parameters of high pour point oil is of great significance to the oil field. In this study, a comprehensive model of the temperature distribution of the wellbore fluid was constructed, and the function of f(tD) was introduced to participate in the calculation of the thermal resistance of the formation. The temperature distribution along the wellbore was calculated and simulated under different conditions. The results show that the surface water injection temperature of 60.8℃ could ensure the desired water temperature reaching the target layer, and the injection temperature of 60.4°C could meet the needs of efficient development after considering the design of the 750-m insulated tubing. The final thermal recovery parameter optimization system could provide a theoretical reference for the thermal recovery design of the same type of reservoir.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131346554","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}
Supriya Sinha, A. Walmsley, N. Clegg, Brígido Vicuña, Hsu-hsiang Wu, A. McGill, Téo Paiva dos Reis, Marianne Therese Nygård, Gunn Åshild Ulfsnes, Monica Vik Constable, F. Antonsen, B. Danielsen
With the introduction of ultradeep azimuthal resistivity (UDAR) logging-while-drilling (LWD) tools toward the beginning of the last decade, the oil and gas industry went from real-time mapping of formation boundaries a few meters from the wellbore to tens of meters away. This innovation allowed early identification of resistivity boundaries and promoted proactive geosteering, allowing for optimization of the wellbore position. Additionally, boundaries and secondary targets that may never be intersected are mapped, allowing for improved well planning for sidetracks, multilaterals, and future wells. Modern tool design and inversion algorithms allow mapping the reservoir in 3D and exploring the sensitivity of these tools to the electromagnetic field ahead of the measure point for look-ahead resistivity. Improvements in the technology over the past decade have changed the way wellbores are planned, drilled, and completed, and reservoir models are updated. This paper presents a case study summarizing the advances in UDAR measurements and inversions over the last decade. The case study presents the whole workflow from prejob planning, service design, and execution of one-dimensional (1D) and three-dimensional (3D) inversion in addition to the future potential of look ahead in horizontal wells. Prewell simulations provide a guide to expected real-time tool responses in highly heterogeneous formations. This identifies how far from the wellbore 1D inversions can map major boundaries above and below the well. A fault was expected toward the toe of the well, and UDAR was used as a safeguard to avoid exiting the reservoir. Standard 1D inversion approaches are too simplistic in this complex geologic setting. Thus, 3D inversion around the wellbore and ahead of the transmitter is also explored to demonstrate the improvements this understanding can bring regarding geostopping toward the fault and reservoir understanding in general. Successful geosteering requires personnel trained to handle complex scenarios. Geosteering training simulators (GTS) could be efficient tools for training to interpret inversions where the “truth” is known from realistic 3D model scenarios. The team can learn how to best exploit UDAR technology and inversion results within its limits and not extend the interpretation beyond acceptable uncertainty levels. It will also be addressed how the understanding of inversion uncertainty could be updated in real time in the future. The continued future success of UDAR technology and 1D to 3D inversion results for look-ahead and look-around applications will depend heavily on uncertainty management of the inversions to avoid wrong decisions and potentially reduced well economy.
{"title":"Past, Present, and Future Applications of Ultradeep Directional Resistivity Measurements: A Case History From the Norwegian Continental Shelf","authors":"Supriya Sinha, A. Walmsley, N. Clegg, Brígido Vicuña, Hsu-hsiang Wu, A. McGill, Téo Paiva dos Reis, Marianne Therese Nygård, Gunn Åshild Ulfsnes, Monica Vik Constable, F. Antonsen, B. Danielsen","doi":"10.30632/pjv63n6-2022a3","DOIUrl":"https://doi.org/10.30632/pjv63n6-2022a3","url":null,"abstract":"With the introduction of ultradeep azimuthal resistivity (UDAR) logging-while-drilling (LWD) tools toward the beginning of the last decade, the oil and gas industry went from real-time mapping of formation boundaries a few meters from the wellbore to tens of meters away. This innovation allowed early identification of resistivity boundaries and promoted proactive geosteering, allowing for optimization of the wellbore position. Additionally, boundaries and secondary targets that may never be intersected are mapped, allowing for improved well planning for sidetracks, multilaterals, and future wells. Modern tool design and inversion algorithms allow mapping the reservoir in 3D and exploring the sensitivity of these tools to the electromagnetic field ahead of the measure point for look-ahead resistivity. Improvements in the technology over the past decade have changed the way wellbores are planned, drilled, and completed, and reservoir models are updated. This paper presents a case study summarizing the advances in UDAR measurements and inversions over the last decade. The case study presents the whole workflow from prejob planning, service design, and execution of one-dimensional (1D) and three-dimensional (3D) inversion in addition to the future potential of look ahead in horizontal wells. Prewell simulations provide a guide to expected real-time tool responses in highly heterogeneous formations. This identifies how far from the wellbore 1D inversions can map major boundaries above and below the well. A fault was expected toward the toe of the well, and UDAR was used as a safeguard to avoid exiting the reservoir. Standard 1D inversion approaches are too simplistic in this complex geologic setting. Thus, 3D inversion around the wellbore and ahead of the transmitter is also explored to demonstrate the improvements this understanding can bring regarding geostopping toward the fault and reservoir understanding in general. Successful geosteering requires personnel trained to handle complex scenarios. Geosteering training simulators (GTS) could be efficient tools for training to interpret inversions where the “truth” is known from realistic 3D model scenarios. The team can learn how to best exploit UDAR technology and inversion results within its limits and not extend the interpretation beyond acceptable uncertainty levels. It will also be addressed how the understanding of inversion uncertainty could be updated in real time in the future. The continued future success of UDAR technology and 1D to 3D inversion results for look-ahead and look-around applications will depend heavily on uncertainty management of the inversions to avoid wrong decisions and potentially reduced well economy.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129031325","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}
Pub Date : 2022-12-01DOI: 10.30632/pjv63n6-2022a12
F. Antonsen, B. Danielsen, Kåre Røsvik Jensen, Marta Prymak-Moyle, J. K. Lotsberg, Maria Emilia Teixeira De Oliveira, Monica Vik Constable
Equinor has played an important role in the last decade in the testing and development of ultradeep azimuthal resistivity (UDAR) measurements both for look-ahead and look-around applications. Today, UDAR technology is applied in more than 70% of Equinor’s high-angle or horizontal wells. In this paper, the authors will review the use of UDAR in Equinor over the last decade and highlight both successful use and real-time challenges related to the interpretation of the inversion results. UDAR technology and inversion algorithms have been very powerful for reservoir mapping to geosteer or geostop according to plan. However, we forget far too often the fact that we need a good understanding of the reservoir to interpret and evaluate the uncertainty in the inversion result. The number one mistake in a real-time setting is to interpret a resistivity contrast as a specific layer in the reservoir (for instance, top reservoir) and hold on to that same interpretation, even if we drill away from that contrast and may cross multiple layers as distance to the observed contrast increases. Other challenging real-time UDAR exercises relate to uncertainties in the prediction of resistivity inside the reservoir and reservoir thickness from inversion results when still drilling above the reservoir. A third mistake often seen in real time is the detailed interpretation of one-dimensional (1D) inversion results, even when other indicators are pointing towards two-dimensional (2D)/three-dimensional (3D) complexities in the reservoir. Equinor and other operators have pushed for more and more advanced inversion solutions, leading to 3D mapping capabilities for more complex reservoirs. The UDAR advances over the last few years are important for Equinor’s planned roadmap ahead. However, 1D through 3D inversion results can result in bad decisions if the uncertainty in the inversion result is not managed correctly. We see a need to investigate how to best exploit UDAR technology and inversion results without extending assumptions beyond an acceptable uncertainty level. Better handling of uncertainties in geosteering operations will become increasingly important for the well economy with smaller targets, complex geological settings, and varying sweep efficiencies. How can we best handle the uncertainty in inversion results in real-time operations to avoid inaccurate decisions that can potentially destroy well economy? This is an important question that will be addressed and should be handled in the future if UDAR technology is to continue its important role in well placement in the next decades.
{"title":"What Next After a Decade With Significant Advances in the Application of Ultradeep Azimuthal Resistivity Measurements?","authors":"F. Antonsen, B. Danielsen, Kåre Røsvik Jensen, Marta Prymak-Moyle, J. K. Lotsberg, Maria Emilia Teixeira De Oliveira, Monica Vik Constable","doi":"10.30632/pjv63n6-2022a12","DOIUrl":"https://doi.org/10.30632/pjv63n6-2022a12","url":null,"abstract":"Equinor has played an important role in the last decade in the testing and development of ultradeep azimuthal resistivity (UDAR) measurements both for look-ahead and look-around applications. Today, UDAR technology is applied in more than 70% of Equinor’s high-angle or horizontal wells. In this paper, the authors will review the use of UDAR in Equinor over the last decade and highlight both successful use and real-time challenges related to the interpretation of the inversion results. UDAR technology and inversion algorithms have been very powerful for reservoir mapping to geosteer or geostop according to plan. However, we forget far too often the fact that we need a good understanding of the reservoir to interpret and evaluate the uncertainty in the inversion result. The number one mistake in a real-time setting is to interpret a resistivity contrast as a specific layer in the reservoir (for instance, top reservoir) and hold on to that same interpretation, even if we drill away from that contrast and may cross multiple layers as distance to the observed contrast increases. Other challenging real-time UDAR exercises relate to uncertainties in the prediction of resistivity inside the reservoir and reservoir thickness from inversion results when still drilling above the reservoir. A third mistake often seen in real time is the detailed interpretation of one-dimensional (1D) inversion results, even when other indicators are pointing towards two-dimensional (2D)/three-dimensional (3D) complexities in the reservoir. Equinor and other operators have pushed for more and more advanced inversion solutions, leading to 3D mapping capabilities for more complex reservoirs. The UDAR advances over the last few years are important for Equinor’s planned roadmap ahead. However, 1D through 3D inversion results can result in bad decisions if the uncertainty in the inversion result is not managed correctly. We see a need to investigate how to best exploit UDAR technology and inversion results without extending assumptions beyond an acceptable uncertainty level. Better handling of uncertainties in geosteering operations will become increasingly important for the well economy with smaller targets, complex geological settings, and varying sweep efficiencies. How can we best handle the uncertainty in inversion results in real-time operations to avoid inaccurate decisions that can potentially destroy well economy? This is an important question that will be addressed and should be handled in the future if UDAR technology is to continue its important role in well placement in the next decades.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117066064","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}
Pub Date : 2022-12-01DOI: 10.30632/pjv63n6-2022a10
V. Simoes, H. Maniar, A. Abubakar, T. Zhao
Researchers have dedicated numerous applications of machine-learning (ML) techniques for fi eld-scale automated interpretation of well-log data. A critical prerequisite for automatic log processing is to ensure that the log characteristics are reasonably consistent across multiple wells. Manually correcting logs for consistency is laborious, subjective, and error prone. For some wellbore logs, such as gamma ray and neutron porosity, borehole effects and miscalibration can cause systematic inconsistencies or errors that might be present even after the application of wellbore and environmental corrections. Biased or consistently inaccurate data in the logs can confound ML approaches into learning erroneous relationships, leading to misinterpretations, such as wrong lithology prediction, reservoir estimation, and incorrect formation markers. To overcome such difficulties, we have developed a deep learning method to provide petrophysicists with a set of consistent logs through the multiwell automatic log correction (MALC) workflow. Presently, the corrections we target are systematic errors on the standard logs, especially gamma ray and neutron logs, random noises, and to a lesser extent, local formation property misreading due to washouts. We applied the proposed method in multiple fi elds worldwide containing different challenges, and in this paper, we include the results in two fi eld examples. The first one covers the correction of synthetic coherent noise added to fi eld data, and the second example covers the correction applied to original measurements.
{"title":"Deep Learning for Multiwell Automatic Log Correction","authors":"V. Simoes, H. Maniar, A. Abubakar, T. Zhao","doi":"10.30632/pjv63n6-2022a10","DOIUrl":"https://doi.org/10.30632/pjv63n6-2022a10","url":null,"abstract":"Researchers have dedicated numerous applications of machine-learning (ML) techniques for fi eld-scale automated interpretation of well-log data. A critical prerequisite for automatic log processing is to ensure that the log characteristics are reasonably consistent across multiple wells. Manually correcting logs for consistency is laborious, subjective, and error prone. For some wellbore logs, such as gamma ray and neutron porosity, borehole effects and miscalibration can cause systematic inconsistencies or errors that might be present even after the application of wellbore and environmental corrections. Biased or consistently inaccurate data in the logs can confound ML approaches into learning erroneous relationships, leading to misinterpretations, such as wrong lithology prediction, reservoir estimation, and incorrect formation markers. To overcome such difficulties, we have developed a deep learning method to provide petrophysicists with a set of consistent logs through the multiwell automatic log correction (MALC) workflow. Presently, the corrections we target are systematic errors on the standard logs, especially gamma ray and neutron logs, random noises, and to a lesser extent, local formation property misreading due to washouts. We applied the proposed method in multiple fi elds worldwide containing different challenges, and in this paper, we include the results in two fi eld examples. The first one covers the correction of synthetic coherent noise added to fi eld data, and the second example covers the correction applied to original measurements.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124697754","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}
Pub Date : 2022-12-01DOI: 10.30632/pjv63n6-2022a11
M. A. Ibrahim
Well-log correlation is used extensively to generate subsurface cross sections from sparse well data. This is commonly done by a subject matter expert such as stratigraphers and exploration geophysicists. Several methodologies exist for automating the procedure with varying success. Dynamic time warping (DTW) is a signal-processing technique where one signal is locally stretched and squeezed to maximize the similarity between a reference second signal. This is done by calculating a similarity cost matrix that is traversed to minimize the cumulative distance. The technique produces reasonable results when applied to the well correlation problem. The produced correlation, however, is deterministic, and thus, it does not allow for studying the associated uncertainty. This study presents an extension of traditional dynamic time warping to allow the generation of multiple realizations of correlations. To accomplish this, the cost matrix is traversed deterministically or probabilistically based on a local correlation metric, e.g., the local correlation coefficient. The resultant realizations show stability in the correlation markers where the signals are similar and instability where they are not. The methodology is applied to two adjacent wells. Multiple well-log types (gamma ray, sonic, and resistivity) are used to construct the similarity cost matrix between the two wells. The cost matrix is traversed multiple times to produce multiple realizations. The produced realizations are geologically acceptable. By generating a large number of realizations, the uncertainty in the solutions is quantified. While the application presented here relates to well-log correlation, the presented stochastic dynamic time warping methodology can be applied to other types of signals and data, such as seismic, chemostratigraphy data, and real-time drilling measurements.
{"title":"Uncertainty in Automated Well-Log Correlation Using Stochastic Dynamic Time Warping","authors":"M. A. Ibrahim","doi":"10.30632/pjv63n6-2022a11","DOIUrl":"https://doi.org/10.30632/pjv63n6-2022a11","url":null,"abstract":"Well-log correlation is used extensively to generate subsurface cross sections from sparse well data. This is commonly done by a subject matter expert such as stratigraphers and exploration geophysicists. Several methodologies exist for automating the procedure with varying success. Dynamic time warping (DTW) is a signal-processing technique where one signal is locally stretched and squeezed to maximize the similarity between a reference second signal. This is done by calculating a similarity cost matrix that is traversed to minimize the cumulative distance. The technique produces reasonable results when applied to the well correlation problem. The produced correlation, however, is deterministic, and thus, it does not allow for studying the associated uncertainty. This study presents an extension of traditional dynamic time warping to allow the generation of multiple realizations of correlations. To accomplish this, the cost matrix is traversed deterministically or probabilistically based on a local correlation metric, e.g., the local correlation coefficient. The resultant realizations show stability in the correlation markers where the signals are similar and instability where they are not. The methodology is applied to two adjacent wells. Multiple well-log types (gamma ray, sonic, and resistivity) are used to construct the similarity cost matrix between the two wells. The cost matrix is traversed multiple times to produce multiple realizations. The produced realizations are geologically acceptable. By generating a large number of realizations, the uncertainty in the solutions is quantified. While the application presented here relates to well-log correlation, the presented stochastic dynamic time warping methodology can be applied to other types of signals and data, such as seismic, chemostratigraphy data, and real-time drilling measurements.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116593997","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}
Nanoindentation is a new technology slowly gaining acceptance in the oil and gas community. In an effort to accelerate its adoption, we review its capabilities and applications. The technology was developed to study thin film and semiconductor properties. It became attractive to the oil and gas industry when focus switched to unconventional shales. Because of the friability and instability of shales, retrieving core plugs for standard measurements became impossible in many cases. Nanoindentation is perfectly adapted to measuring the properties of fine-grained materials and material fragments. We document how nanoindentation can be used to measure Young’s modulus, hardness, shear modulus, anisotropy, creep, and fracture toughness and to examine the fluid sensitivity of these properties in shale.
{"title":"A Guide to Nanoindentation","authors":"C. Sondergeld, C. Rai","doi":"10.30632/pjv63n5-2022a1","DOIUrl":"https://doi.org/10.30632/pjv63n5-2022a1","url":null,"abstract":"Nanoindentation is a new technology slowly gaining acceptance in the oil and gas community. In an effort to accelerate its adoption, we review its capabilities and applications. The technology was developed to study thin film and semiconductor properties. It became attractive to the oil and gas industry when focus switched to unconventional shales. Because of the friability and instability of shales, retrieving core plugs for standard measurements became impossible in many cases. Nanoindentation is perfectly adapted to measuring the properties of fine-grained materials and material fragments. We document how nanoindentation can be used to measure Young’s modulus, hardness, shear modulus, anisotropy, creep, and fracture toughness and to examine the fluid sensitivity of these properties in shale.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123904326","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}
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