{"title":"Flow Regimes-Based Decline Curve for Unconventional Reservoirs: Generalization to Anomalous Diffusion and Power Law Behavior","authors":"V. Artus, O. Houzé, Chih-Cheng Chen","doi":"10.15530/URTEC-2019-293","DOIUrl":"https://doi.org/10.15530/URTEC-2019-293","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124636","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 : 2019-07-31DOI: 10.15530/urtec-2019-1001
Carrie Glaser, J. Mazza, J. Frame
{"title":"Empowering Completion Engineers to Calibrate Petrophysical Facies Models to Hydraulic Fracturing Treatment Responses","authors":"Carrie Glaser, J. Mazza, J. Frame","doi":"10.15530/urtec-2019-1001","DOIUrl":"https://doi.org/10.15530/urtec-2019-1001","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116743746","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}
Seismic attributes can be both powerful and challenging to incorporate into interpretation and analysis. Recent developments with machine learning have added new capabilities to multi-attribute seismic analysis. In 2018, Geophysical Insights conducted a proof of concept on 100 square miles of multi-client 3D data jointly owned by Geophysical Pursuit, Inc. (GPI) and Fairfield Geotechnologies (FFG) in the Denver-Julesburg Basin (DJ). The purpose of the study was to evaluate the effectiveness of a machine learning workflow to improve resolution within the reservoir intervals of the Niobrara and Codell formations, the primary targets for development in this portion of the basin. The seismic data are from Phase 5 of the GPI/Fairfield Niobrara program in northern Colorado. A preliminary workflow which included synthetics, horizon picking and correlation of 28 wells was completed. The seismic volume was re-sampled from 2 ms to 1 ms. Detailed well time-depth charts were created for the Top Niobrara, Niobrara A, B and C benches, Fort Hays and Codell intervals. The interpretations, along with the seismic volume, were loaded into the Paradise® machine learning application, and two suites of attributes were generated, instantaneous and geometric. The first step in the machine learning workflow is Principal Component Analysis (PCA). PCA is a method of identifying attributes that have the greatest contribution to the data and that quantifies the relative contribution of each. PCA aids in the selection of which attributes are appropriate to use in a Self-Organizing Map (SOM). In this case, 15 instantaneous attribute volumes, plus the parent amplitude volume, were used in the PCA and eight were selected to use in SOMs. The SOM is a neural network-based machine learning process that is applied to multiple attribute volumes simultaneously. The SOM produces a non-linear classification of the data in a designated time or depth window. For this study, a 60-ms interval that encompasses the Niobrara and Codell formations was evaluated using several SOM topologies. One of the main drilling targets, the B chalk, is approximately 30 feet thick; making horizontal well planning and execution a challenge for operators. An 8 X 8 SOM applied to 1 ms seismic data improves the stratigraphic resolution of the B bench. The neuron classification also images small but significant structural variations within the chalk bench. These variations correlate visually with the geometric curvature attributes. This improved resolution allows for precise well planning for horizontals within the bench. The 25 foot thick C bench and the 17 to 25 foot thick Codell are also seismically resolved via SOM analysis. Petrophysical analyses from wireline logs run in seven wells
{"title":"Machine Learning Applied to 3-D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara","authors":"C. Laudon, Sarah Stanley, P. Santogrossi","doi":"10.15530/URTEC-2019-337","DOIUrl":"https://doi.org/10.15530/URTEC-2019-337","url":null,"abstract":"Seismic attributes can be both powerful and challenging to incorporate into interpretation and analysis. Recent developments with machine learning have added new capabilities to multi-attribute seismic analysis. In 2018, Geophysical Insights conducted a proof of concept on 100 square miles of multi-client 3D data jointly owned by Geophysical Pursuit, Inc. (GPI) and Fairfield Geotechnologies (FFG) in the Denver-Julesburg Basin (DJ). The purpose of the study was to evaluate the effectiveness of a machine learning workflow to improve resolution within the reservoir intervals of the Niobrara and Codell formations, the primary targets for development in this portion of the basin. The seismic data are from Phase 5 of the GPI/Fairfield Niobrara program in northern Colorado. A preliminary workflow which included synthetics, horizon picking and correlation of 28 wells was completed. The seismic volume was re-sampled from 2 ms to 1 ms. Detailed well time-depth charts were created for the Top Niobrara, Niobrara A, B and C benches, Fort Hays and Codell intervals. The interpretations, along with the seismic volume, were loaded into the Paradise® machine learning application, and two suites of attributes were generated, instantaneous and geometric. The first step in the machine learning workflow is Principal Component Analysis (PCA). PCA is a method of identifying attributes that have the greatest contribution to the data and that quantifies the relative contribution of each. PCA aids in the selection of which attributes are appropriate to use in a Self-Organizing Map (SOM). In this case, 15 instantaneous attribute volumes, plus the parent amplitude volume, were used in the PCA and eight were selected to use in SOMs. The SOM is a neural network-based machine learning process that is applied to multiple attribute volumes simultaneously. The SOM produces a non-linear classification of the data in a designated time or depth window. For this study, a 60-ms interval that encompasses the Niobrara and Codell formations was evaluated using several SOM topologies. One of the main drilling targets, the B chalk, is approximately 30 feet thick; making horizontal well planning and execution a challenge for operators. An 8 X 8 SOM applied to 1 ms seismic data improves the stratigraphic resolution of the B bench. The neuron classification also images small but significant structural variations within the chalk bench. These variations correlate visually with the geometric curvature attributes. This improved resolution allows for precise well planning for horizontals within the bench. The 25 foot thick C bench and the 17 to 25 foot thick Codell are also seismically resolved via SOM analysis. Petrophysical analyses from wireline logs run in seven wells","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124910558","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}
{"title":"The Effect of Surface-Gas Interaction on Mean Free Path for Gases Confined in Nanopores of Shale Gas Reservoirs","authors":"Yan-ling Gao, Keliu Wu, Sheng Yang, Xiaohu Dong, Zhongliang Chen, Chen Zhangxing","doi":"10.15530/URTEC-2019-284","DOIUrl":"https://doi.org/10.15530/URTEC-2019-284","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590481","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}
A. Ortiz, B. Sauerer, Jean-Paul Lafournère, P. Saldungaray, Wael Abdallah
{"title":"Raman Spectroscopy Based Maturity Profiling of the Vaca Muerta Formation, Neuquén Basin, Argentina","authors":"A. Ortiz, B. Sauerer, Jean-Paul Lafournère, P. Saldungaray, Wael Abdallah","doi":"10.15530/urtec-2019-425","DOIUrl":"https://doi.org/10.15530/urtec-2019-425","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122683638","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}
Lucas Mejía, A. Mehmani, M. Balhoff, C. Torres‐Verdín
{"title":"Process-Based Microfluidics: Tools for Quantifying the Impact of Reservoir Quality on Recovery Factor","authors":"Lucas Mejía, A. Mehmani, M. Balhoff, C. Torres‐Verdín","doi":"10.15530/URTEC-2019-888","DOIUrl":"https://doi.org/10.15530/URTEC-2019-888","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131184691","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}
Helen Hammon, Timothy J. Prather, Harry Rowe, P. Mainali, M. Matheny, R. Krumm
The latest Pennsylvanian and Early Permian (Wolfcamp, Dean, and Spraberry interval) of the Midland Basin, West Texas, represents a thick (often >1000 feet), mixed succession of shale, carbonate, and siltstone/sandstone lithologies that accumulated in a deep-water environment under variable hydrographic restriction. The succession is a prime target for petroleum companies working in the Permian Basin, of which the Midland Basin is an integral part. Because the succession is very thick and lithologically variable, it is critical to understand and predict the stratigraphic and lateral variability of the rocks. A highly-resolved (2-inch vertical) XRF-based chemostratigraphic study was undertaken on the Sun Oil D.E. Richards #1 drill core, recovered from Martin Co., Texas. While the core does not preserve a continuous record of the interval, it does contain long, uninterrupted sections of the upper Wolfcamp shale/siltstone through the lowermost Clearfork equivalent strata, just above the uppermost Spraberry operational unit. Major and trace element analyses were conducted on the slabbed core face using a Bruker Tracer IV-SD ED-XRF spectrometer. Elemental concentrations for 2567 sample intervals were calibrated from raw x-ray spectra using a set of reference materials developed from a broad range of mudrock lithologies (Rowe et al., 2012), and a subset of depth-matched sample powders (n = 229) was collected from the back of the core for mineralogical (XRD) and organic carbon analysis (LECO).
{"title":"Geochemical, Mineralogical, and Lithological Linkages in a Thick, Early Permian, Siliciclastic Succession, Midland Basin, West Texas, USA","authors":"Helen Hammon, Timothy J. Prather, Harry Rowe, P. Mainali, M. Matheny, R. Krumm","doi":"10.15530/URTEC-2019-454","DOIUrl":"https://doi.org/10.15530/URTEC-2019-454","url":null,"abstract":"The latest Pennsylvanian and Early Permian (Wolfcamp, Dean, and Spraberry interval) of the Midland Basin, West Texas, represents a thick (often >1000 feet), mixed succession of shale, carbonate, and siltstone/sandstone lithologies that accumulated in a deep-water environment under variable hydrographic restriction. The succession is a prime target for petroleum companies working in the Permian Basin, of which the Midland Basin is an integral part. Because the succession is very thick and lithologically variable, it is critical to understand and predict the stratigraphic and lateral variability of the rocks. A highly-resolved (2-inch vertical) XRF-based chemostratigraphic study was undertaken on the Sun Oil D.E. Richards #1 drill core, recovered from Martin Co., Texas. While the core does not preserve a continuous record of the interval, it does contain long, uninterrupted sections of the upper Wolfcamp shale/siltstone through the lowermost Clearfork equivalent strata, just above the uppermost Spraberry operational unit. Major and trace element analyses were conducted on the slabbed core face using a Bruker Tracer IV-SD ED-XRF spectrometer. Elemental concentrations for 2567 sample intervals were calibrated from raw x-ray spectra using a set of reference materials developed from a broad range of mudrock lithologies (Rowe et al., 2012), and a subset of depth-matched sample powders (n = 229) was collected from the back of the core for mineralogical (XRD) and organic carbon analysis (LECO).","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128036755","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}
{"title":"Salt Water Disposal Modeling of Dakota Sand, Williston Basin, to Drive Drilling Decisions","authors":"S. Basu, T. Cross, S. Skvortsov","doi":"10.15530/URTEC-2019-488","DOIUrl":"https://doi.org/10.15530/URTEC-2019-488","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128048939","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}
Shane Butler, A. Azenkeng, B. Mibeck, B. Kurz, K. Eylands
{"title":"Unconventional Rock Requires Unconventional Analysis: Methods for Characterization","authors":"Shane Butler, A. Azenkeng, B. Mibeck, B. Kurz, K. Eylands","doi":"10.15530/URTEC-2019-971","DOIUrl":"https://doi.org/10.15530/URTEC-2019-971","url":null,"abstract":"","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132691568","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}
T. Carr, P. Ghahfarokhi, B. Carney, Jay Hewitt, Robert Vargnetti
The Marcellus Shale Energy and Environment Laboratory (MSEEL) involves a multidisciplinary and multi-institutional team of universities companies and government research labs undertaking geologic and geomechanical evaluation, integrated completion and production monitoring, and testing completion approaches. MSEEL consists of two legacy horizontal production wells, two new logged and instrumented horizontal production wells, a cored vertical pilot bore-hole, a microseismic observation well, and surface geophysical and environmental monitoring stations. The extremely large and diverse (multiple terabyte) datasets required a custom software system for analysis and display of fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data that was subsequently integrated with microseismic data, core data and logs from the pilot holes and laterals. Comprehensive geomechanical and image log data integrated with the fiber-optic data across individual stages and clusters contributed to an improved understanding of the effect of stage spacing and cluster density practices across the heterogeneous unconventional reservoirs such as the Marcellus. The results significantly improved stimulation effectiveness and optimized recovery efficiency. The microseismic and fiber-optic data obtained during the hydraulic fracture simulations and subsequent DTS data acquired during production served as constraining parameters to evaluate stage and cluster efficiency on the MIP3H and MIP-5H wells. Deformation effects related to preexisting fractures and small faults are a significant component to improve understanding of completion quality differences between stages and clusters. The distribution of this deformation and cross-flow between stages as shown by the DAS and DTS fiber-optic data during stimulation demonstrates the differences in completion efficiency among stages. The initial and evolving production efficiency over the last several years of various stages is illustrated through ongoing processing of continuous DTS. Reservoir simulation and history matching the well production data confirmed the subsurface production response to the hydraulic fractures. Engineered stages that incorporate the distribution of fracture swarms and geomechanical properties had better completion and more importantly production efficiencies. We are working to improve the modeling to understand movement within individual fracture swarms and history match at the individual
{"title":"Marcellus Shale Energy and Environmental Laboratory (MSEEL) Results and Plans: Improved Subsurface Reservoir Characterization And Engineered Completions","authors":"T. Carr, P. Ghahfarokhi, B. Carney, Jay Hewitt, Robert Vargnetti","doi":"10.15530/URTEC-2019-415","DOIUrl":"https://doi.org/10.15530/URTEC-2019-415","url":null,"abstract":"The Marcellus Shale Energy and Environment Laboratory (MSEEL) involves a multidisciplinary and multi-institutional team of universities companies and government research labs undertaking geologic and geomechanical evaluation, integrated completion and production monitoring, and testing completion approaches. MSEEL consists of two legacy horizontal production wells, two new logged and instrumented horizontal production wells, a cored vertical pilot bore-hole, a microseismic observation well, and surface geophysical and environmental monitoring stations. The extremely large and diverse (multiple terabyte) datasets required a custom software system for analysis and display of fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) data that was subsequently integrated with microseismic data, core data and logs from the pilot holes and laterals. Comprehensive geomechanical and image log data integrated with the fiber-optic data across individual stages and clusters contributed to an improved understanding of the effect of stage spacing and cluster density practices across the heterogeneous unconventional reservoirs such as the Marcellus. The results significantly improved stimulation effectiveness and optimized recovery efficiency. The microseismic and fiber-optic data obtained during the hydraulic fracture simulations and subsequent DTS data acquired during production served as constraining parameters to evaluate stage and cluster efficiency on the MIP3H and MIP-5H wells. Deformation effects related to preexisting fractures and small faults are a significant component to improve understanding of completion quality differences between stages and clusters. The distribution of this deformation and cross-flow between stages as shown by the DAS and DTS fiber-optic data during stimulation demonstrates the differences in completion efficiency among stages. The initial and evolving production efficiency over the last several years of various stages is illustrated through ongoing processing of continuous DTS. Reservoir simulation and history matching the well production data confirmed the subsurface production response to the hydraulic fractures. Engineered stages that incorporate the distribution of fracture swarms and geomechanical properties had better completion and more importantly production efficiencies. We are working to improve the modeling to understand movement within individual fracture swarms and history match at the individual","PeriodicalId":432911,"journal":{"name":"Proceedings of the 7th Unconventional Resources Technology Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132903808","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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