Along this active exploration belt, applying the conventional effective stress methods and algorithms, where maximum stress (S1) is vertical would lead to unintended and unrealistic results. In the frontier active thrust belt of the Gulf of Mexico, the unique geomechanical setting of S1 as the lateral salt creep, and the minimum (S3) as the OB greatly impact the formation geopressure framework. The buoyancy of thick salt produces two different pressure gradients above and below the salt. Moreover, the inclusion of rafted sediments in the salt and the plowing rubble zone at the salt base substantially affect the pore and fracture pressures (PP-FP) profiles. These proceeding geological setting were the foundation for the conceptual framework. Building an alternative pre-drilling prediction numerical model based on this anomalous geomechanical settings and the lack of adequate seismic velocity is a challenge. All the available measured or pertained PP-FP data from key wells were tabulated. Prediction models were established by correlating the populated database and generating the empirical algorithm for each data gather. A substantial discrepancy between above and below the salt where high pressure gradient (PG) in the sediment above the salt and slow PG development below the salt. A considerable regressive pressure (average 2 ppg) in both PP-FP subsalt section. The PP within the salt is contingent on the presence of sediment inclusions and a substantial FP drop in the rubble zone leads to extensive loss of mud circulation. The trend lines of each data gather led to generating two depth dependent equations for the PP-FP above and within the salt and two others for the subsalt. The prediction models were validated against blind data set. Before drilling, this model establishes the PP-FP vs. sediment subsea depth in an abnormal geomechanical setting and the lack of coherent seismic velocity for PP -FP prediction.
{"title":"Pore and Fracture Pressures prediction new geomechanic approach in Deepwater Salt Overthrusts, Case histories from Gulf of Mexico","authors":"S. Shaker","doi":"10.1190/int-2023-0109.1","DOIUrl":"https://doi.org/10.1190/int-2023-0109.1","url":null,"abstract":"Along this active exploration belt, applying the conventional effective stress methods and algorithms, where maximum stress (S1) is vertical would lead to unintended and unrealistic results. In the frontier active thrust belt of the Gulf of Mexico, the unique geomechanical setting of S1 as the lateral salt creep, and the minimum (S3) as the OB greatly impact the formation geopressure framework. The buoyancy of thick salt produces two different pressure gradients above and below the salt. Moreover, the inclusion of rafted sediments in the salt and the plowing rubble zone at the salt base substantially affect the pore and fracture pressures (PP-FP) profiles. These proceeding geological setting were the foundation for the conceptual framework. Building an alternative pre-drilling prediction numerical model based on this anomalous geomechanical settings and the lack of adequate seismic velocity is a challenge. All the available measured or pertained PP-FP data from key wells were tabulated. Prediction models were established by correlating the populated database and generating the empirical algorithm for each data gather. A substantial discrepancy between above and below the salt where high pressure gradient (PG) in the sediment above the salt and slow PG development below the salt. A considerable regressive pressure (average 2 ppg) in both PP-FP subsalt section. The PP within the salt is contingent on the presence of sediment inclusions and a substantial FP drop in the rubble zone leads to extensive loss of mud circulation. The trend lines of each data gather led to generating two depth dependent equations for the PP-FP above and within the salt and two others for the subsalt. The prediction models were validated against blind data set. Before drilling, this model establishes the PP-FP vs. sediment subsea depth in an abnormal geomechanical setting and the lack of coherent seismic velocity for PP -FP prediction.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"31 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800660","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}
Calibrating process-based geological models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geological model is used as a training image for statistical pattern recognition. First, we define a rock physics model from the process-based geological model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geological pattern recognition and regeneration under seismic data constraint. The method differs from the conventional MPS method in several ways: 1) The training image is a process-based geological model of the reservoir of interest, thus defined on the same grid of the reservoir model; 2) The training image is generally non-stationary, but there is no need to partition the non-stationary training image into pseudo-stationary ones; 3) The geological facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; 4) Multiple-point statistics are based on Bayes law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; 5) The method does not involve an iterative optimization procedure. So, it also differs from the neural-network-based machine learning approach where the data conditioning is achieved through an iterative optimization procedure. These differences make the proposed method advantageous for calibrating process-based geological models. The two examples with synthetic data illustrate the effectiveness of the method.
{"title":"REGIONALIZED MULTIPLE-POINT STATISTICAL SIMULATION FOR CALIBRATING PROCESS-BASED GEOLOGICAL MODELS TO SEISMIC DATA","authors":"Lin Ying Hu, Yupeng Li","doi":"10.1190/int-2023-0123.1","DOIUrl":"https://doi.org/10.1190/int-2023-0123.1","url":null,"abstract":"Calibrating process-based geological models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geological model is used as a training image for statistical pattern recognition. First, we define a rock physics model from the process-based geological model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geological pattern recognition and regeneration under seismic data constraint. The method differs from the conventional MPS method in several ways: 1) The training image is a process-based geological model of the reservoir of interest, thus defined on the same grid of the reservoir model; 2) The training image is generally non-stationary, but there is no need to partition the non-stationary training image into pseudo-stationary ones; 3) The geological facies and seismic constraint are related through seismic forward modeling instead of statistical inference, thus there is no need to convert seismic data to facies proportion or probability; 4) Multiple-point statistics are based on Bayes law and Gaussian kernel approximation of conditional probability instead of a somehow arbitrary probability combination scheme or a heuristic rule; 5) The method does not involve an iterative optimization procedure. So, it also differs from the neural-network-based machine learning approach where the data conditioning is achieved through an iterative optimization procedure. These differences make the proposed method advantageous for calibrating process-based geological models. The two examples with synthetic data illustrate the effectiveness of the method.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"47 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799645","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 organic carbon content is the main index for evaluating organic matter abundance of source rocks, and it is still difficult to quantitatively predict TOC in source rocks based on seismic data. The study of seismic fluid identification driven by petrophysics can help to understand the fluid characteristics and distribution patterns of subsurface oil and gas reservoirs. This paper, first of all, clarified the sweet spot parameters (parameters characterizing hydrocarbon enrichment) and sensitive elastic parameters (a parameter characterizing the nature of an ideal elastic body) of source rocks through theoretical petrophysical modeling, and established the relationship between sensitive elastic parameters and sweet spot parameters TOC, and construct a statistical petrophysical model that can characterize the relationship between the two on this basis. And then construct the joint distribution of TOC and elastic impedance through the Bayesian theoretical framework to obtain the maximum posterior probability estimate as the final TOC inversion results of source rocks. Our method successfully predicts the spreading of high-quality source rocks in the Wen4 Section of Lufeng 13 Subsag, and the inversion results are within an uncertainty range of ±14 m for well data, which proves the reliability of the method. The prediction results show that the organic matter abundance of source rocks in the Wen4 Section is high and the organic carbon content is generally higher than 2%, which provides a reliable basis for the further implementation of the resource scale of the depression and the clarification of the hydrocarbon rich area, which provides technical support for the evaluation of the source rocks of the new depression in the new area.
{"title":"A petrophysically driven seismic inversion method for TOC content of hydrocarbon source rocks","authors":"Zhangbo Xiao, Heming Lin, Weiwei Zhang, Ming Luo, Xudong Wang, Zhiwei Zhang","doi":"10.1190/int-2023-0060.1","DOIUrl":"https://doi.org/10.1190/int-2023-0060.1","url":null,"abstract":"The organic carbon content is the main index for evaluating organic matter abundance of source rocks, and it is still difficult to quantitatively predict TOC in source rocks based on seismic data. The study of seismic fluid identification driven by petrophysics can help to understand the fluid characteristics and distribution patterns of subsurface oil and gas reservoirs. This paper, first of all, clarified the sweet spot parameters (parameters characterizing hydrocarbon enrichment) and sensitive elastic parameters (a parameter characterizing the nature of an ideal elastic body) of source rocks through theoretical petrophysical modeling, and established the relationship between sensitive elastic parameters and sweet spot parameters TOC, and construct a statistical petrophysical model that can characterize the relationship between the two on this basis. And then construct the joint distribution of TOC and elastic impedance through the Bayesian theoretical framework to obtain the maximum posterior probability estimate as the final TOC inversion results of source rocks. Our method successfully predicts the spreading of high-quality source rocks in the Wen4 Section of Lufeng 13 Subsag, and the inversion results are within an uncertainty range of ±14 m for well data, which proves the reliability of the method. The prediction results show that the organic matter abundance of source rocks in the Wen4 Section is high and the organic carbon content is generally higher than 2%, which provides a reliable basis for the further implementation of the resource scale of the depression and the clarification of the hydrocarbon rich area, which provides technical support for the evaluation of the source rocks of the new depression in the new area.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"90 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818721","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}
Cem Menlikli, Fearghal Hayes, Jiaqi Zhao, Andrew Wakelin, Zhigang Xu, Hongyao Fan, Don Umbsaar, Kent Wilkinson, Kristian Lomas, Chris Purcell
Seismic fluid indicators are highly accurate when calibrated, but when the data is scarce, an interpreter must choose between different indicators. We investigate the data from multiple basins to understand the most effective fluid indicators for use. Unlike previous studies, the data comprises a large, equal, and statistically meaningful number of hydrocarbon and brine sands from different basins. We show that Poisson’s Ratio–LambdaRho templates can be used to separate different facies in synthetic and real data and can be used as a useful crossplot interpretation tool. We propose a fluid indicator, as LambdaRho Poisson’s Ratio multiplication, (λρ)σ, which is highly sensitive to saturation and can be potentially used as a fizz-gas discriminator. We compare the sensitivity of different fluid indicators to hydrocarbon saturation by using Bhattacharyya Distance, which provides a quantitative metric for how well different indicators separate hydrocarbon and brine sands, and it is a non-parametric measure unlike other fluid indicator scores proposed in similar studies. Absolute properties derived from seismic by inversion are rarely available in regional studies, whereas relative elastic properties can be easily obtained and used. Following the concepts of elastic reflectivity vectors and geometry of intercept-gradient crossplots, we show how the reflectivity of different fluid indicators can be approximated from AVO parameters at various chi ( χ) angles, enabling an interpreter to use them even when inversion products are not available. Finally, we compare the effectiveness of relative elastic parameters on different AVO classes and show that no single attribute works best across all classes, but for general screening purposes Fluid Factor and R((λρ)σ) can be good choices. The findings of this study can help better characterization of fluids in exploration, appraisal, and development of hydrocarbons, and in other areas where monitoring produced and injected fluids is important like 4-D seismic or Carbon Capture Storage.
{"title":"Poisson's Ratio-LambdaRho rock physics templates and a study on sensitivity of different fluid indicators","authors":"Cem Menlikli, Fearghal Hayes, Jiaqi Zhao, Andrew Wakelin, Zhigang Xu, Hongyao Fan, Don Umbsaar, Kent Wilkinson, Kristian Lomas, Chris Purcell","doi":"10.1190/int-2024-0003.1","DOIUrl":"https://doi.org/10.1190/int-2024-0003.1","url":null,"abstract":"Seismic fluid indicators are highly accurate when calibrated, but when the data is scarce, an interpreter must choose between different indicators. We investigate the data from multiple basins to understand the most effective fluid indicators for use. Unlike previous studies, the data comprises a large, equal, and statistically meaningful number of hydrocarbon and brine sands from different basins. We show that Poisson’s Ratio–LambdaRho templates can be used to separate different facies in synthetic and real data and can be used as a useful crossplot interpretation tool. We propose a fluid indicator, as LambdaRho Poisson’s Ratio multiplication, (λρ)σ, which is highly sensitive to saturation and can be potentially used as a fizz-gas discriminator. We compare the sensitivity of different fluid indicators to hydrocarbon saturation by using Bhattacharyya Distance, which provides a quantitative metric for how well different indicators separate hydrocarbon and brine sands, and it is a non-parametric measure unlike other fluid indicator scores proposed in similar studies. Absolute properties derived from seismic by inversion are rarely available in regional studies, whereas relative elastic properties can be easily obtained and used. Following the concepts of elastic reflectivity vectors and geometry of intercept-gradient crossplots, we show how the reflectivity of different fluid indicators can be approximated from AVO parameters at various chi ( χ) angles, enabling an interpreter to use them even when inversion products are not available. Finally, we compare the effectiveness of relative elastic parameters on different AVO classes and show that no single attribute works best across all classes, but for general screening purposes Fluid Factor and R((λρ)σ) can be good choices. The findings of this study can help better characterization of fluids in exploration, appraisal, and development of hydrocarbons, and in other areas where monitoring produced and injected fluids is important like 4-D seismic or Carbon Capture Storage.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"89 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818818","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}
Despite extensive exploration efforts in the Potwar Basin, a thorough understanding of the structural framework and reservoir characteristics within the Meyal Field remains critical for pinpointing potential hydrocarbon zones. This study aims to integrate geology and geophysics to emphasize the structural and stratigraphic interpretation of the Meyal Field, including reservoir characterization using seismic and well-log data. The seismic interpretation of six seismic lines focused on four key reflectors: the Kamlial, Chorgali, Sakesar, and Salt Range Formations. The Eocene Chorgali and Sakesar Formations are of primary importance for exploration and production. Structural analysis revealed the Meyal anticline to be a plunging structure bounded by a back-thrust to the north and a fore-thrust to the south, suggesting a favorable location for hydrocarbon accumulation. Time-depth contour maps derived from seismic data further delineate potential sites for future investigations. Well correlation indicated an uplift towards Meyal-2 compared to Meyal-5, signifying a shallowing trend attributed to thrust tectonics. This tectonic regime has rendered the area highly prospective for hydrocarbon exploration, with thrusting and oblique-slip faulting enhancing the reservoir qualities of Eocene formations. The petrophysical evaluation revealed favorable reservoir characteristics, including an average porosity ranging from 0% to 12% with an effective porosity of approximately 7.5%, water saturation up to 42%, and hydrocarbon saturation reaching 58% within the pay zone of the reservoir. These findings suggest that the Sakesar and Chorgali Formations within the Meyal oil field hold promise as productive hydrocarbon reservoirs. The integrated study provides valuable insights into the structural framework and reservoir characterization, highlighting the potential of Eocene formations as productive hydrocarbon reservoirs supported by favorable structural configurations and petrophysical properties.
{"title":"SEISMIC REFLECTION DATA INTERPRETATION AND PETROPHYSICAL EVALUATION OF MEYAL AREA, POTWAR BASIN, PAKISTAN","authors":"Manawar Pervaiz, Yanfei Wang","doi":"10.1190/int-2023-0096.1","DOIUrl":"https://doi.org/10.1190/int-2023-0096.1","url":null,"abstract":"Despite extensive exploration efforts in the Potwar Basin, a thorough understanding of the structural framework and reservoir characteristics within the Meyal Field remains critical for pinpointing potential hydrocarbon zones. This study aims to integrate geology and geophysics to emphasize the structural and stratigraphic interpretation of the Meyal Field, including reservoir characterization using seismic and well-log data. The seismic interpretation of six seismic lines focused on four key reflectors: the Kamlial, Chorgali, Sakesar, and Salt Range Formations. The Eocene Chorgali and Sakesar Formations are of primary importance for exploration and production. Structural analysis revealed the Meyal anticline to be a plunging structure bounded by a back-thrust to the north and a fore-thrust to the south, suggesting a favorable location for hydrocarbon accumulation. Time-depth contour maps derived from seismic data further delineate potential sites for future investigations. Well correlation indicated an uplift towards Meyal-2 compared to Meyal-5, signifying a shallowing trend attributed to thrust tectonics. This tectonic regime has rendered the area highly prospective for hydrocarbon exploration, with thrusting and oblique-slip faulting enhancing the reservoir qualities of Eocene formations. The petrophysical evaluation revealed favorable reservoir characteristics, including an average porosity ranging from 0% to 12% with an effective porosity of approximately 7.5%, water saturation up to 42%, and hydrocarbon saturation reaching 58% within the pay zone of the reservoir. These findings suggest that the Sakesar and Chorgali Formations within the Meyal oil field hold promise as productive hydrocarbon reservoirs. The integrated study provides valuable insights into the structural framework and reservoir characterization, highlighting the potential of Eocene formations as productive hydrocarbon reservoirs supported by favorable structural configurations and petrophysical properties.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"100 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818333","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}
Marcus Vinicius Rodrigues Maas, Heather Bedle, Mario Ricardo Ballinas, Marcilio Castro de Matos
During the initial phases of an EP project, the most reliable data on the reservoirs deliverability are acquired via drill stem tests (DST), which provide productivity per flow units, whenever production logging tool (PLT) data are available. However, DSTs are restricted to a few kilometers, whereas seismic data cover large areas. The integration of these data has been challenging, particularly due to the difference in scale between them. So, a new workflow to determine the relationship between post-stack seismic attributes and reservoir productivity using classic supervised (shallow) and deep learning regression algorithms was proposed. The DST parameters were predicted over the entire seismic cube, which can be extremely valuable for the decision-making process. The dataset is from the Brazilian deep water pre-salt carbonate reservoirs of the Mero Field, which is a well explored area with a plethora of test and production data. It is adjacent to an underexplored area (Central Libra appraisal plan), which is covered by the same seismic survey. Thus, any relationships between seismic attributes and well productivity data observed at the Mero field are extrapolated to the adjacent underexplored area. Ten seismic attributes and DST data from ten wells of Mero Field were used to train shallow and deep learning supervised regression algorithms for the prediction of flow capacity and productivity index seismic cubes. Twenty development wells (blind tests) were employed for the assessment of our predictive models. The highest percentage of correct predictions at the blind test wells (85%) was obtained with random forest regression using six attributes derived from a spectrally balanced full-stack volume, neither AVO nor inversion data were needed. Deep learning provided lower performance (75%) at a higher computational cost. It demonstrated a new reservoir de-risking tool that can be used for project optimization in areas covered by the same seismic survey.
在 EP 项目的初始阶段，只要有生产测井仪器（PLT）数据，就可以通过钻杆测试（DST）获得有关储层可开采性的最可靠数据。然而，钻杆测试的范围仅限于几公里，而地震数据则覆盖大片区域。这些数据的整合具有挑战性，特别是由于它们之间的规模差异。因此，我们提出了一种新的工作流程，利用经典的监督（浅层）和深度学习回归算法来确定叠后地震属性与储层产能之间的关系。对整个地震立方体的 DST 参数进行了预测，这对决策过程极具价值。数据集来自巴西 Mero 油田的深水前盐碳酸盐岩储层，这是一个勘探良好的地区，有大量的测试和生产数据。它毗邻一个勘探不足的地区（Central Libra 评估计划），该地区也在同一地震勘探范围内。因此，在梅罗油田观察到的地震属性和油井生产数据之间的任何关系都可以推断到邻近的未充分勘探区。梅罗油田的十个地震属性和十口井的 DST 数据被用于训练浅层和深度学习监督回归算法，以预测流动能力和产能指数地震立方体。采用 20 口开发井（盲测）对我们的预测模型进行评估。在盲测井中，采用随机森林回归法预测的正确率最高（85%），该方法使用了从频谱平衡的全叠层卷中提取的六个属性，既不需要 AVO 数据，也不需要反演数据。深度学习的性能较低（75%），但计算成本较高。它展示了一种新的储层去风险工具，可用于同一地震勘探覆盖区域的项目优化。
{"title":"Unraveling hidden relationships between seismic multi-attributes, well dynamic data, and Brazilian pre-salt carbonate reservoirs productivity: a shallow versus deep machine learning approach.","authors":"Marcus Vinicius Rodrigues Maas, Heather Bedle, Mario Ricardo Ballinas, Marcilio Castro de Matos","doi":"10.1190/int-2023-0113.1","DOIUrl":"https://doi.org/10.1190/int-2023-0113.1","url":null,"abstract":"During the initial phases of an EP project, the most reliable data on the reservoirs deliverability are acquired via drill stem tests (DST), which provide productivity per flow units, whenever production logging tool (PLT) data are available. However, DSTs are restricted to a few kilometers, whereas seismic data cover large areas. The integration of these data has been challenging, particularly due to the difference in scale between them. So, a new workflow to determine the relationship between post-stack seismic attributes and reservoir productivity using classic supervised (shallow) and deep learning regression algorithms was proposed. The DST parameters were predicted over the entire seismic cube, which can be extremely valuable for the decision-making process. The dataset is from the Brazilian deep water pre-salt carbonate reservoirs of the Mero Field, which is a well explored area with a plethora of test and production data. It is adjacent to an underexplored area (Central Libra appraisal plan), which is covered by the same seismic survey. Thus, any relationships between seismic attributes and well productivity data observed at the Mero field are extrapolated to the adjacent underexplored area. Ten seismic attributes and DST data from ten wells of Mero Field were used to train shallow and deep learning supervised regression algorithms for the prediction of flow capacity and productivity index seismic cubes. Twenty development wells (blind tests) were employed for the assessment of our predictive models. The highest percentage of correct predictions at the blind test wells (85%) was obtained with random forest regression using six attributes derived from a spectrally balanced full-stack volume, neither AVO nor inversion data were needed. Deep learning provided lower performance (75%) at a higher computational cost. It demonstrated a new reservoir de-risking tool that can be used for project optimization in areas covered by the same seismic survey.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"15 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344038","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}
Compelling geoscience evidence has heightened appreciation of abnormal (supra-hydrostatic, sub-/quasi-/supra-lithostatic) pore pressure and multi-mode (brittle-shear) fault rupture leading to seismicity. However, preparation and rupture nucleation are yet to be adequately constrained and quantitatively modelled. This challenges crucial schemes, notably physics-based earthquake forecasting/prediction. In this multidisciplinary study, transitions and associated critical points, linking pre-exisiting fluid overpressure in fault patches, temporal crustal stress and sequential brittle-shear fault failure, were considered. In preparation, tectonic loading was accompanied by processes such as off-fault yielding, permeability enhancement and foreshocks that facilitate local/regional stress relaxation. Subsequent equalization of stress and pre-exisiting local overpressure triggers hydraulic fracturing that destabilizes major asperities. Almost instant shear failure follows with spatially varying rupture velocity/intensity of frictional slip because of localized asperity stress and syn-slip fluid-/melt-driven fracturing/dilation and lubrication. Based on aforementioned critical points, quantitative modelling associated stress drop with evolution of stress and pore pressure. Equations for time to onset of stress relaxation and time to rupture were derived using fluid flow and viscoelastic models. Seismic moment was estimated with classical seismological relations after modifications accounting for less surface area during frictional slip. For retrospective testing, two cases of induced seismicity (2016 Fairview, Oklahoma USA and 2017 Pohang, South Korea) and multiple cases of natural seismicity (including the 2024 Noto Peninsula Earthquake, Japan), were considered. Replication of triggering mechanisms, source properties and time to rupture suggested that stress temporal relaxation and triggered anomalies (STRATA) encompass fundamental hydromechanical processes in seismogenesis. Setting and scale invariance of STRATA suggest it might be a general theory of earthquake nucleation. Based on identified preparatory processes/retrospective validations, a physics-based earthquake forecasting/prediction scheme was proposed. Nurseries/hypocenters of impending earthquakes are identified through simultaneous consideration of locally pre-existing fluid overpressure and spatiotemporal analysis of stress relaxation. Event size and rupture timing are estimated with derived relations herein.
{"title":"Crustal Stress Build-Up/Relaxation and Pore Pressure in Preparation and Sequential Brittle-Shear Fault Rupture Implications for a General Theory of Earthquake Nucleation","authors":"Clay Kurison","doi":"10.1190/int-2023-0007.1","DOIUrl":"https://doi.org/10.1190/int-2023-0007.1","url":null,"abstract":"Compelling geoscience evidence has heightened appreciation of abnormal (supra-hydrostatic, sub-/quasi-/supra-lithostatic) pore pressure and multi-mode (brittle-shear) fault rupture leading to seismicity. However, preparation and rupture nucleation are yet to be adequately constrained and quantitatively modelled. This challenges crucial schemes, notably physics-based earthquake forecasting/prediction. In this multidisciplinary study, transitions and associated critical points, linking pre-exisiting fluid overpressure in fault patches, temporal crustal stress and sequential brittle-shear fault failure, were considered. In preparation, tectonic loading was accompanied by processes such as off-fault yielding, permeability enhancement and foreshocks that facilitate local/regional stress relaxation. Subsequent equalization of stress and pre-exisiting local overpressure triggers hydraulic fracturing that destabilizes major asperities. Almost instant shear failure follows with spatially varying rupture velocity/intensity of frictional slip because of localized asperity stress and syn-slip fluid-/melt-driven fracturing/dilation and lubrication. Based on aforementioned critical points, quantitative modelling associated stress drop with evolution of stress and pore pressure. Equations for time to onset of stress relaxation and time to rupture were derived using fluid flow and viscoelastic models. Seismic moment was estimated with classical seismological relations after modifications accounting for less surface area during frictional slip. For retrospective testing, two cases of induced seismicity (2016 Fairview, Oklahoma USA and 2017 Pohang, South Korea) and multiple cases of natural seismicity (including the 2024 Noto Peninsula Earthquake, Japan), were considered. Replication of triggering mechanisms, source properties and time to rupture suggested that stress temporal relaxation and triggered anomalies (STRATA) encompass fundamental hydromechanical processes in seismogenesis. Setting and scale invariance of STRATA suggest it might be a general theory of earthquake nucleation. Based on identified preparatory processes/retrospective validations, a physics-based earthquake forecasting/prediction scheme was proposed. Nurseries/hypocenters of impending earthquakes are identified through simultaneous consideration of locally pre-existing fluid overpressure and spatiotemporal analysis of stress relaxation. Event size and rupture timing are estimated with derived relations herein.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"7 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266060","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}
Crosswell strain measurements acquired through low-frequency Distributed Acoustic Sensing (LF-DAS) is a maturing technique used to monitor and diagnose the efficiency of hydraulic fracturing treatments. While LF-DAS has demonstrated potential in characterizing far-field fracture communication and geometry, the prevailing analysis in this field has historically relied on qualitative interpretations, focusing on the timing and location of frac hits. In response to the evolving landscape of quantitative studies in this area, we present an advanced quantitative technique using our novel Green-function based inversion algorithm to calculate time-dependent far-field fracture width. The adopted algorithm utilizes the 3D displacement discontinuity method to relate fracture aperture to strains measured by LF-DAS along a monitor well during stimulation treatments. This approach is demonstrated on a subset of four treatment stages where Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) were acquired within the injection well, alongside LF-DAS from a nearby monitor well. LF-DAS inversion results indicate the loss of zonal isolation in three of the monitored stages, leading to significantly smaller fracture widths at the monitor well for targeted treatment stages, and reactivated fractures in the adjacent previous stages. These interpretations are cross validated through the integration of in-well DAS and DTS analysis, where severe inter-stage fluid communication is observed. The inverted fracture widths quantify the impact on far-field fracture geometry associated with poor stage isolation and decreased fracturing efficiency. This new approach demonstrates the potential of LF-DAS for quantitative analysis and interpretation, facilitating improved understanding and optimization of hydraulic stimulation, going beyond its conventional qualitative role in fracturing diagnostics.
{"title":"QUANTITATIVE MULTISTAGE FRACTURING EFFICIENCY EVALUATION USING CROSS-WELL STRAIN MEASUREMENTS","authors":"Joseph Mjehovich, Ge Jin, K. Wu","doi":"10.1190/int-2023-0083.1","DOIUrl":"https://doi.org/10.1190/int-2023-0083.1","url":null,"abstract":"Crosswell strain measurements acquired through low-frequency Distributed Acoustic Sensing (LF-DAS) is a maturing technique used to monitor and diagnose the efficiency of hydraulic fracturing treatments. While LF-DAS has demonstrated potential in characterizing far-field fracture communication and geometry, the prevailing analysis in this field has historically relied on qualitative interpretations, focusing on the timing and location of frac hits. In response to the evolving landscape of quantitative studies in this area, we present an advanced quantitative technique using our novel Green-function based inversion algorithm to calculate time-dependent far-field fracture width. The adopted algorithm utilizes the 3D displacement discontinuity method to relate fracture aperture to strains measured by LF-DAS along a monitor well during stimulation treatments. This approach is demonstrated on a subset of four treatment stages where Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) were acquired within the injection well, alongside LF-DAS from a nearby monitor well. LF-DAS inversion results indicate the loss of zonal isolation in three of the monitored stages, leading to significantly smaller fracture widths at the monitor well for targeted treatment stages, and reactivated fractures in the adjacent previous stages. These interpretations are cross validated through the integration of in-well DAS and DTS analysis, where severe inter-stage fluid communication is observed. The inverted fracture widths quantify the impact on far-field fracture geometry associated with poor stage isolation and decreased fracturing efficiency. This new approach demonstrates the potential of LF-DAS for quantitative analysis and interpretation, facilitating improved understanding and optimization of hydraulic stimulation, going beyond its conventional qualitative role in fracturing diagnostics.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"51 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269567","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}
Pulau Tiga is Malaysia’s largest mud volcano and the entire island is famously reported to have suddenly appeared in September 1897. The story of Pulau Tiga’s ‘birth’ following two large earthquakes in the Philippines is widespread, being found in scientific papers, media stories, Wikipedia and thousands of websites. This study ‘fact checks’ whether Pulau Tiga really did first appear in 1897. The event is described in detail in one 1986 scientific study, and the idea of Pulau Tiga’s sudden appearance is supported by instances of islands being created by large mud volcano eruptions, including Pulau Batu Hairan offshore Malaysia in 1988. However, maps of Borneo published prior to 1897 show that Pulau Tiga was charted on maps dating back to 1554, and specifically named on over 40 maps. These maps conclusively prove that Pulau Tiga did not suddenly appear in 1897, and also indicate that Pulau Tiga has had approximately the same shape for centuries, demonstrating the island was also not partially formed in 1897. A review of newspapers and scientific reports from 1897 to 1904 reveal that two new islands did emerge offshore northern Borneo on the 21st of September 1897, but neither was Pulau Tiga. New mud volcano islands temporarily appeared off the western Klias Peninsula near Bukit Tomboh, and off the northern tip of Sabah at the approximate position of Pulau Batu Hairan. The origin of Pulau Tiga’s 1897 birth ‘myth’ is proposed to be a 1986 study by McManus and Tate, who appear to have confused reports of an island forming off the Klias Peninsula in 1897 with an eruption of Pulau Tiga. This study definitively ‘busts’ the myth of Pulau Tiga’s 1897 birth and unearths details of Malaysia’s history of large mud volcano eruptions that have been largely forgotten by the geoscience community.
{"title":"Myth-busting: Was Pulau Tiga Really First Created by a Mud Volcano Eruption in 1897?","authors":"Mark R. P. Tingay","doi":"10.1190/int-2024-0013.1","DOIUrl":"https://doi.org/10.1190/int-2024-0013.1","url":null,"abstract":"Pulau Tiga is Malaysia’s largest mud volcano and the entire island is famously reported to have suddenly appeared in September 1897. The story of Pulau Tiga’s ‘birth’ following two large earthquakes in the Philippines is widespread, being found in scientific papers, media stories, Wikipedia and thousands of websites. This study ‘fact checks’ whether Pulau Tiga really did first appear in 1897. The event is described in detail in one 1986 scientific study, and the idea of Pulau Tiga’s sudden appearance is supported by instances of islands being created by large mud volcano eruptions, including Pulau Batu Hairan offshore Malaysia in 1988. However, maps of Borneo published prior to 1897 show that Pulau Tiga was charted on maps dating back to 1554, and specifically named on over 40 maps. These maps conclusively prove that Pulau Tiga did not suddenly appear in 1897, and also indicate that Pulau Tiga has had approximately the same shape for centuries, demonstrating the island was also not partially formed in 1897. A review of newspapers and scientific reports from 1897 to 1904 reveal that two new islands did emerge offshore northern Borneo on the 21st of September 1897, but neither was Pulau Tiga. New mud volcano islands temporarily appeared off the western Klias Peninsula near Bukit Tomboh, and off the northern tip of Sabah at the approximate position of Pulau Batu Hairan. The origin of Pulau Tiga’s 1897 birth ‘myth’ is proposed to be a 1986 study by McManus and Tate, who appear to have confused reports of an island forming off the Klias Peninsula in 1897 with an eruption of Pulau Tiga. This study definitively ‘busts’ the myth of Pulau Tiga’s 1897 birth and unearths details of Malaysia’s history of large mud volcano eruptions that have been largely forgotten by the geoscience community.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"68 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101501","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}
Tim P. Dooley, Juan I. Soto, Jacqueline E. Reber, Michael R. Hudec, Frank J. Peel, Gillian M. Apps
Weak substrates, such as salt and mobile shales, exert a strong control on deformation styles in all structural settings, especially those undergoing contraction. Despite both materials being very weak, they are mechanically very different. Salt is weak and will flow in a ductile fashion under most geologic conditions, whereas shales only become mobile after reaching critical state. Many sandbox-style physical or analog modeling studies have typically utilized a salt analog, viscous silicone polymer, as a proxy for mobile shales. However, to more accurately model mobile shale behavior the model material needs to exhibit a yield strength. One such material is Carbopol which is made up of micro-gel grains that are elasto-plastic, separated by a viscous interstitial ?uid. The abundance of the grains depends on the concentration of the mixture. Our results show that Carbopol does behave much differently than the traditional salt analog during contraction. PDMS typically undergoes bulk deformation and inflation under contraction, whereas Carbopol forms discrete, intense shear zones, and contains zones of little to no strain where its yield strength has not been exceeded. Below the shale analog, brittle layers typically form imbricate thrust stacks, jacking up the overburden, with shear zones propagating out from thrust tips along and through the shale proxy. Strain analyses reveal complex switching of activity within the Carbopol and overlying sediments. Models reveal that even a very thin layer of Carbopol can act as a highly-efficient detachment, and form more geologically realistic shortening structures, especially where these detachments are vertically stacked and horizontally offset. We believe that Carbopol is a powerful mobile-shale analog and opens new modeling directions because, as far as we are aware, this material has never been incorporated into a traditional sandbox model. Future work will seek to incorporate this material into more complex and three-dimensional sandbox-style models.
{"title":"MODELING MOBILE SHALES UNDER CONTRACTION: CRITICAL ANALYSES OF NEW ANALOG SIMULATIONS OF SHALE TECTONICS AND COMPARISON WITH SALT-BEARING SYSTEMS","authors":"Tim P. Dooley, Juan I. Soto, Jacqueline E. Reber, Michael R. Hudec, Frank J. Peel, Gillian M. Apps","doi":"10.1190/int-2024-0025.1","DOIUrl":"https://doi.org/10.1190/int-2024-0025.1","url":null,"abstract":"Weak substrates, such as salt and mobile shales, exert a strong control on deformation styles in all structural settings, especially those undergoing contraction. Despite both materials being very weak, they are mechanically very different. Salt is weak and will flow in a ductile fashion under most geologic conditions, whereas shales only become mobile after reaching critical state. Many sandbox-style physical or analog modeling studies have typically utilized a salt analog, viscous silicone polymer, as a proxy for mobile shales. However, to more accurately model mobile shale behavior the model material needs to exhibit a yield strength. One such material is Carbopol which is made up of micro-gel grains that are elasto-plastic, separated by a viscous interstitial ?uid. The abundance of the grains depends on the concentration of the mixture. Our results show that Carbopol does behave much differently than the traditional salt analog during contraction. PDMS typically undergoes bulk deformation and inflation under contraction, whereas Carbopol forms discrete, intense shear zones, and contains zones of little to no strain where its yield strength has not been exceeded. Below the shale analog, brittle layers typically form imbricate thrust stacks, jacking up the overburden, with shear zones propagating out from thrust tips along and through the shale proxy. Strain analyses reveal complex switching of activity within the Carbopol and overlying sediments. Models reveal that even a very thin layer of Carbopol can act as a highly-efficient detachment, and form more geologically realistic shortening structures, especially where these detachments are vertically stacked and horizontally offset. We believe that Carbopol is a powerful mobile-shale analog and opens new modeling directions because, as far as we are aware, this material has never been incorporated into a traditional sandbox model. Future work will seek to incorporate this material into more complex and three-dimensional sandbox-style models.","PeriodicalId":502519,"journal":{"name":"Interpretation","volume":"29 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141107925","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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