Yumna Al Habsi, A. Anbari, Azzan Al Yaarubi, R. Leech, Sumaiya Al Bimani, S. Choudhury
Perseverance in quantifying the remaining hydrocarbon saturation, in cased boreholes, remains critical to take business decisions and prioritize operations in brownfield waterflood development. Challenges with cased hole saturation evaluation acquired in certain complex completions such as those completed in multiple casing-tubing strings, slotted-liners and sand-screens require advanced tool technology. Pulsed Neutron Logging (PNL) is one such technology used successfully to analyze behind casing saturation evaluation. The PNL device provide accurate and precise measurement, and with robust processing and environmental compensation corrections, the saturation uncertainty can be delineated. A robust cased hole hydrocarbon saturation and uncertainty estimation enables informed decision making and value driven workover prioritization. The new generation PNL tool features a high-output electronic neutron source and four signal detectors. Near and far Gamma Ray (GR) detectors are made of Cerium-doped Lanthanum Bromide (LaBr3: Ce) featuring high-count rate efficiency and high-spectral resolution (largely insensitive to temperatures variations). A deep-reading GR detector made of Yttrium Aluminum Perovskite (YAP) in combination with a compact fast neutron monitor placed adjacent to the neutron source, enables a new measurement of the fast neutron cross section (FNXS) which provides sensitivity to gas-filled porosity. A newly devised pulsing scheme allows simultaneous measurement in both time and energy domains. The time-domain measurement aid in analyzing the self-compensated capture cross section (SIGM), neutron porosity (TPHI), and FNXS. The energy-domain measurement provides a detailed insight for high-precision mineralogy, total organic carbon (TOC), and carbon/oxygen ratio (COR). The high statistical precision energy-domain capture and inelastic spectral yield data are interpreted using an oxide-closure model which when combined with an extensive tool characterization database provide lithology and saturation measurements compensated for wellbore and completion contributions. This paper shares the advanced features of the new multi-detector PNL tool run in a horizontal well targeting the aeolian Mahwis Formation, consisting of unconsolidated sands and the glacial Al Khlata Formation (Porosity ranges 0.25 – 0.29 p.u.). In this case-study, the well was completed with uncemented sand screens and production tubing to mitigate sanding related risk. The absence of cement behind casing and the presence of screens adds considerable complexity to the saturation analysis. Furthermore, due to low water salinity (∼7000 ppm NaCl equivalent), saturation must be determined using carbon spectroscopy-based techniques - namely the COR and TOC. Logging conventional PNL tools in horizontal wells can lead to lengthy acquisition times, thus adding considerable operational complexity and cost. With the new PNL technology advancements, the time required to acquire hi
{"title":"Unlocking Growth Opportunities Through Saturation Evaluation Behind Complex Completion by Applying State-of-Art Pulsed Neutron Technology","authors":"Yumna Al Habsi, A. Anbari, Azzan Al Yaarubi, R. Leech, Sumaiya Al Bimani, S. Choudhury","doi":"10.2118/204783-ms","DOIUrl":"https://doi.org/10.2118/204783-ms","url":null,"abstract":"\u0000 Perseverance in quantifying the remaining hydrocarbon saturation, in cased boreholes, remains critical to take business decisions and prioritize operations in brownfield waterflood development. Challenges with cased hole saturation evaluation acquired in certain complex completions such as those completed in multiple casing-tubing strings, slotted-liners and sand-screens require advanced tool technology. Pulsed Neutron Logging (PNL) is one such technology used successfully to analyze behind casing saturation evaluation. The PNL device provide accurate and precise measurement, and with robust processing and environmental compensation corrections, the saturation uncertainty can be delineated. A robust cased hole hydrocarbon saturation and uncertainty estimation enables informed decision making and value driven workover prioritization.\u0000 The new generation PNL tool features a high-output electronic neutron source and four signal detectors. Near and far Gamma Ray (GR) detectors are made of Cerium-doped Lanthanum Bromide (LaBr3: Ce) featuring high-count rate efficiency and high-spectral resolution (largely insensitive to temperatures variations). A deep-reading GR detector made of Yttrium Aluminum Perovskite (YAP) in combination with a compact fast neutron monitor placed adjacent to the neutron source, enables a new measurement of the fast neutron cross section (FNXS) which provides sensitivity to gas-filled porosity. A newly devised pulsing scheme allows simultaneous measurement in both time and energy domains. The time-domain measurement aid in analyzing the self-compensated capture cross section (SIGM), neutron porosity (TPHI), and FNXS. The energy-domain measurement provides a detailed insight for high-precision mineralogy, total organic carbon (TOC), and carbon/oxygen ratio (COR). The high statistical precision energy-domain capture and inelastic spectral yield data are interpreted using an oxide-closure model which when combined with an extensive tool characterization database provide lithology and saturation measurements compensated for wellbore and completion contributions.\u0000 This paper shares the advanced features of the new multi-detector PNL tool run in a horizontal well targeting the aeolian Mahwis Formation, consisting of unconsolidated sands and the glacial Al Khlata Formation (Porosity ranges 0.25 – 0.29 p.u.). In this case-study, the well was completed with uncemented sand screens and production tubing to mitigate sanding related risk. The absence of cement behind casing and the presence of screens adds considerable complexity to the saturation analysis. Furthermore, due to low water salinity (∼7000 ppm NaCl equivalent), saturation must be determined using carbon spectroscopy-based techniques - namely the COR and TOC. Logging conventional PNL tools in horizontal wells can lead to lengthy acquisition times, thus adding considerable operational complexity and cost. With the new PNL technology advancements, the time required to acquire hi","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74373573","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}
Elemental chemostratigraphy has become an established stratigraphic correlation technique over the last 15 years. Geochemical data are generated from rock samples (e.g., ditch cuttings, cores or hand specimens) for up to c. 50 elements in the range Na-U in the periodic table using various analytical techniques. The data are commonly displayed and interpreted as ratios, indices and proxy values in profile form against depth. The large number of possible combinations between the determined elements (more than a thousand combinations), makes it a time-consuming effort to identify meaningful variations that resulted in correlative chemostratigraphic boundaries and zones between wells. The large number of combination means that 30-40% of the information is not used for the correlations that maybe crucial to understand the geological processes. Automation and artificial intelligence (AI) are envisaged as likely solutions to this challenge. Statistical and machine learning techniques are tested as a first step to automate and establish a workflow to define (chemo-) stratigraphic boundaries, and to identify geological formations. The workflow commences with a quality check of the input data and then with principle component analysis (PCA) as a multivariate statistical method. PCA is used to minimize the number of elements/ratios plotted in profile form, whilst simultaneously identifying multidimensional relationships between them. A statistical boundary picking method is then applied define chemostratigraphic zones, for which reliability is determined utilizing quartile analysis, which tests the overlap of chemical signals across these statistical boundaries. Machine learning via discriminant function analysis (DFA) has been developed to predict the placement of correlative boundaries between adjacent sections/wells. The proposed workflow has been tested on various geological formations and areas in Saudi Arabia. The chemostratigraphic correlations proposed using this workflow broadly correspond to those defined in the standard workflow by experienced chemostratigraphers, while interpretation times and subjectivity are reduced. While machine learning via DFA is currently further researched, early results of the workflow are very encouraging. A user-friendly software application with workflows and algorithms ultimately leading to automation of the processes is under development.
{"title":"Automations in Chemostratigraphy: Toward Robust Chemical Data Analysis and Interpretation","authors":"N. Michael, C. Scheibe, N. Craigie","doi":"10.2118/204892-ms","DOIUrl":"https://doi.org/10.2118/204892-ms","url":null,"abstract":"\u0000 Elemental chemostratigraphy has become an established stratigraphic correlation technique over the last 15 years. Geochemical data are generated from rock samples (e.g., ditch cuttings, cores or hand specimens) for up to c. 50 elements in the range Na-U in the periodic table using various analytical techniques. The data are commonly displayed and interpreted as ratios, indices and proxy values in profile form against depth. The large number of possible combinations between the determined elements (more than a thousand combinations), makes it a time-consuming effort to identify meaningful variations that resulted in correlative chemostratigraphic boundaries and zones between wells. The large number of combination means that 30-40% of the information is not used for the correlations that maybe crucial to understand the geological processes. Automation and artificial intelligence (AI) are envisaged as likely solutions to this challenge.\u0000 Statistical and machine learning techniques are tested as a first step to automate and establish a workflow to define (chemo-) stratigraphic boundaries, and to identify geological formations. The workflow commences with a quality check of the input data and then with principle component analysis (PCA) as a multivariate statistical method. PCA is used to minimize the number of elements/ratios plotted in profile form, whilst simultaneously identifying multidimensional relationships between them. A statistical boundary picking method is then applied define chemostratigraphic zones, for which reliability is determined utilizing quartile analysis, which tests the overlap of chemical signals across these statistical boundaries. Machine learning via discriminant function analysis (DFA) has been developed to predict the placement of correlative boundaries between adjacent sections/wells.\u0000 The proposed workflow has been tested on various geological formations and areas in Saudi Arabia. The chemostratigraphic correlations proposed using this workflow broadly correspond to those defined in the standard workflow by experienced chemostratigraphers, while interpretation times and subjectivity are reduced.\u0000 While machine learning via DFA is currently further researched, early results of the workflow are very encouraging. A user-friendly software application with workflows and algorithms ultimately leading to automation of the processes is under development.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79367147","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}
S. Kalam, Mohammad Rasheed Khan, Rizwan Ahmed Khan
This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art artificial intelligence (AI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserves calculation and reservoir engineering studies. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can optimize time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of the Middle East region. Multiple AI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, bubble point pressure, and crude oil API gravity. Comparative analysis of developed AI models is performed using visualization/statistical analysis, and the best model is pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented. Graphical analysis is used to evaluate the performance of developed AI models. The results of scatter plots showed most of the points are lying on the 45 degree line. Moreover, during this study, an error metric is developed comprising of multiple analysis parameters; Average absolute percentage error (AAPE), Root Mean Squared Error (RMSE), coefficient of determination (R2). All models investigated are tested on an unseen dataset to prevent a biased model's development. Performance of the established AI models is gauged based on this error metric, demonstrating that ANN outperforms ANFIS with error within 1% of the measured PVT values. A computationally derived intelligent model provides the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.
{"title":"Artificial Intelligence-based Predictive Technique to Estimate Oil Formation Volume Factor","authors":"S. Kalam, Mohammad Rasheed Khan, Rizwan Ahmed Khan","doi":"10.2118/204561-ms","DOIUrl":"https://doi.org/10.2118/204561-ms","url":null,"abstract":"\u0000 This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art artificial intelligence (AI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserves calculation and reservoir engineering studies. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can optimize time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of the Middle East region.\u0000 Multiple AI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, bubble point pressure, and crude oil API gravity. Comparative analysis of developed AI models is performed using visualization/statistical analysis, and the best model is pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented.\u0000 Graphical analysis is used to evaluate the performance of developed AI models. The results of scatter plots showed most of the points are lying on the 45 degree line. Moreover, during this study, an error metric is developed comprising of multiple analysis parameters; Average absolute percentage error (AAPE), Root Mean Squared Error (RMSE), coefficient of determination (R2). All models investigated are tested on an unseen dataset to prevent a biased model's development. Performance of the established AI models is gauged based on this error metric, demonstrating that ANN outperforms ANFIS with error within 1% of the measured PVT values. A computationally derived intelligent model provides the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84177414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an approach by integrating advanced cutting analysis, such as x-ray fluorescence (XRF), and open-hole logs for enhanced formation evaluation of complex clastic formations in near real-time. To verify the methodology, results of surface cuttings analyses are compared to and validated with downhole elemental spectroscopy measurements. In general, when the formation contains clays, the minimum logging requirement to evaluate clastic formations is a triple combo (density, neutron and resistivity) with spectral gamma ray (SGR) logs. In addition to correcting the impact of the drilling fluid additives and properties such as the presence of k-formate in mud, SGR logs become very crucial to differentiate clay types present in the formation. In the absence of SGR, advanced cuttings measurements can be utilized to provide elemental data of major elements including SGR components from the cuttings in near real-time. A comparison was made to evaluate the cuttings analysis as a replacement for SGR. As a part of this work and to validate the petrophysical evaluation results, downhole wireline SGR and elemental spectroscopy data were acquired and compared to the analysis using advanced cutting measurements. This work was conducted in a siliciclastic formation containing abrasive sandstones of mixed clean quartz and clay minerals. The analysis of cuttings XRF was integrated with basic downhole logs to quantify the clay typing required for representative formation evaluation and well geosteering. Limitations of this approach are identified in drilling complex clastic formations including cutting sampling frequency and effects of drilling including drilling fluid contamination, mud additives, drilling parameters and drilling driving mechanism. Controlling these factors has led to good results from cuttings measurements. The advanced cuttings XRF analysis was benchmarked with wireline SGR and elemental spectroscopy logs. This approach of using cuttings XRF analysis and basic open-hole logs is a valid option for geosteering in a complex clastic mineralogy formation and providing a near real-time formation evaluation in the absence of spectral gamma ray or elemental spectroscopy. XRF has been proven to provide near real-time analysis with improved reliability across bad hole, wider spectrum of elements and eliminate critical operations risk. Recommendations to optimize the parameters for reliable measurements will be discussed in this paper.
{"title":"Integration of Cutting Spectroscopy Analysis and Open-Hole Logs to Increase Evaluation Certainty of Complex Clastic Formations – Advantages and Limitations","authors":"Ali Alqunais, Charles Bradford, Khalid Qubaisi","doi":"10.2118/204693-ms","DOIUrl":"https://doi.org/10.2118/204693-ms","url":null,"abstract":"\u0000 This paper presents an approach by integrating advanced cutting analysis, such as x-ray fluorescence (XRF), and open-hole logs for enhanced formation evaluation of complex clastic formations in near real-time. To verify the methodology, results of surface cuttings analyses are compared to and validated with downhole elemental spectroscopy measurements.\u0000 In general, when the formation contains clays, the minimum logging requirement to evaluate clastic formations is a triple combo (density, neutron and resistivity) with spectral gamma ray (SGR) logs. In addition to correcting the impact of the drilling fluid additives and properties such as the presence of k-formate in mud, SGR logs become very crucial to differentiate clay types present in the formation. In the absence of SGR, advanced cuttings measurements can be utilized to provide elemental data of major elements including SGR components from the cuttings in near real-time. A comparison was made to evaluate the cuttings analysis as a replacement for SGR. As a part of this work and to validate the petrophysical evaluation results, downhole wireline SGR and elemental spectroscopy data were acquired and compared to the analysis using advanced cutting measurements.\u0000 This work was conducted in a siliciclastic formation containing abrasive sandstones of mixed clean quartz and clay minerals. The analysis of cuttings XRF was integrated with basic downhole logs to quantify the clay typing required for representative formation evaluation and well geosteering. Limitations of this approach are identified in drilling complex clastic formations including cutting sampling frequency and effects of drilling including drilling fluid contamination, mud additives, drilling parameters and drilling driving mechanism. Controlling these factors has led to good results from cuttings measurements. The advanced cuttings XRF analysis was benchmarked with wireline SGR and elemental spectroscopy logs.\u0000 This approach of using cuttings XRF analysis and basic open-hole logs is a valid option for geosteering in a complex clastic mineralogy formation and providing a near real-time formation evaluation in the absence of spectral gamma ray or elemental spectroscopy. XRF has been proven to provide near real-time analysis with improved reliability across bad hole, wider spectrum of elements and eliminate critical operations risk. Recommendations to optimize the parameters for reliable measurements will be discussed in this paper.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88643048","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. Amer, Ali Alshehri, H. Saiari, Ali Meshaikhis, Abdulaziz Alshamrany
Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types. The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI. This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.
{"title":"Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI","authors":"A. Amer, Ali Alshehri, H. Saiari, Ali Meshaikhis, Abdulaziz Alshamrany","doi":"10.2118/204690-ms","DOIUrl":"https://doi.org/10.2118/204690-ms","url":null,"abstract":"\u0000 Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types.\u0000 The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI.\u0000 This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91001073","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}
W. Fares, I. Moustafa, A. A. Al Felasi, H. Khemissa, O. A. Al Mutwali, F. Gutierrez, N. Clegg, A. Duriez, A. Aki
The high reservoir uncertainty, due to the lateral distribution of fluids, results in variable water saturation, which is very challenging in drilling horizontal wells. In order to reduce uncertainty, the plan was to drill a pilot hole to evaluate the target zones and plan horizontal sections based on the information gained. To investigate the possibility of avoiding pilot holes in the future, an advanced ultra-deep resistivity mapping sensor was deployed to map the mature reservoirs, to identify formation and fluid boundaries early before penetrating them, avoiding the need for pilot holes. Prewell inversion modeling was conducted to optimize the spacing and firing frequency selection and to facilitate an early real-time geostopping decision. The plan was to run the ultra-deep resistivity mapping sensor in conjunction with shallow propagation resistivity, density, and neutron porosity tools while drilling the 8 ½-in. landing section. The real-time ultra-deep resistivity mapping inversion was run using a depth of inversion up to 120 ft., to be able to detect the reservoir early and evaluate the predicted reservoir resistivity. This would allow optimization of any geostopping decision. The ultra-deep resistivity mapping sensor delivered accurate mapping of low resistivity zones up to 85 ft. TVD away from the wellbore in a challenging low resistivity environment. The real-time ultra-deep resistivity mapping inversion enabled the prediction of resistivity values in target zones prior to entering the reservoir; values which were later crosschecked against open-hole logs for validation. The results enabled identification of the optimal geostopping point in the 8 ½-in. section, enabling up to seven rig days to be saved in the future by eliminating a pilot hole. In addition this would eliminate the risk of setting a whipstock at high inclination with the subsequent impact on milling operations. In specific cases, this minimizes drilling risks in unknown/high reservoir pressure zones by improving early detection of formation tops. Plans were modified for a nearby future well and the pilot-hole phase was eliminated because of the confidence provided by these results. Deployment of the ultra-deep resistivity mapping sensor in these mature carbonate reservoirs may reduce the uncertainty associated with fluid migration. In addition, use of the tool can facilitate precise geosteering to maintain distance from fluid boundaries in thick reservoirs. Furthermore, due to the depths of investigation possible with these tools, it will help enable the mapping of nearby reservoirs for future development. Further multi-disciplinary studies remain desirable using existing standard log data to validate the effectiveness of this concept for different fields and reservoirs.
{"title":"An Advanced Ultra-Deep Resistivity Mapping Sensor Reduced Reservoir Uncertainty and Eliminated the Need for a Pilot Hole for the First Time; A Case Study from Offshore Abu Dhabi","authors":"W. Fares, I. Moustafa, A. A. Al Felasi, H. Khemissa, O. A. Al Mutwali, F. Gutierrez, N. Clegg, A. Duriez, A. Aki","doi":"10.2118/204813-ms","DOIUrl":"https://doi.org/10.2118/204813-ms","url":null,"abstract":"\u0000 The high reservoir uncertainty, due to the lateral distribution of fluids, results in variable water saturation, which is very challenging in drilling horizontal wells. In order to reduce uncertainty, the plan was to drill a pilot hole to evaluate the target zones and plan horizontal sections based on the information gained. To investigate the possibility of avoiding pilot holes in the future, an advanced ultra-deep resistivity mapping sensor was deployed to map the mature reservoirs, to identify formation and fluid boundaries early before penetrating them, avoiding the need for pilot holes.\u0000 Prewell inversion modeling was conducted to optimize the spacing and firing frequency selection and to facilitate an early real-time geostopping decision. The plan was to run the ultra-deep resistivity mapping sensor in conjunction with shallow propagation resistivity, density, and neutron porosity tools while drilling the 8 ½-in. landing section. The real-time ultra-deep resistivity mapping inversion was run using a depth of inversion up to 120 ft., to be able to detect the reservoir early and evaluate the predicted reservoir resistivity. This would allow optimization of any geostopping decision.\u0000 The ultra-deep resistivity mapping sensor delivered accurate mapping of low resistivity zones up to 85 ft. TVD away from the wellbore in a challenging low resistivity environment. The real-time ultra-deep resistivity mapping inversion enabled the prediction of resistivity values in target zones prior to entering the reservoir; values which were later crosschecked against open-hole logs for validation. The results enabled identification of the optimal geostopping point in the 8 ½-in. section, enabling up to seven rig days to be saved in the future by eliminating a pilot hole. In addition this would eliminate the risk of setting a whipstock at high inclination with the subsequent impact on milling operations. In specific cases, this minimizes drilling risks in unknown/high reservoir pressure zones by improving early detection of formation tops. Plans were modified for a nearby future well and the pilot-hole phase was eliminated because of the confidence provided by these results.\u0000 Deployment of the ultra-deep resistivity mapping sensor in these mature carbonate reservoirs may reduce the uncertainty associated with fluid migration. In addition, use of the tool can facilitate precise geosteering to maintain distance from fluid boundaries in thick reservoirs. Furthermore, due to the depths of investigation possible with these tools, it will help enable the mapping of nearby reservoirs for future development. Further multi-disciplinary studies remain desirable using existing standard log data to validate the effectiveness of this concept for different fields and reservoirs.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90323150","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}
Alexis Koulidis, Mohamed Abdullatif, Ahmed Galal Abdel-Kader, M. Ayachi, Shehab Ahmed, C. Gooneratne, A. Magana-Mora, Mike Affleck, M. Alsheikh
Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.
{"title":"Field Assessment of Camera Based Drilling Dynamics","authors":"Alexis Koulidis, Mohamed Abdullatif, Ahmed Galal Abdel-Kader, M. Ayachi, Shehab Ahmed, C. Gooneratne, A. Magana-Mora, Mike Affleck, M. Alsheikh","doi":"10.2118/204634-ms","DOIUrl":"https://doi.org/10.2118/204634-ms","url":null,"abstract":"\u0000 Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work.\u0000 Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network.\u0000 We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement.\u0000 Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be \"conscious\" and \"intelligent,\" hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76872638","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}
G. Izadi, C. Barton, P. Roux, Tebis Llobet, T. Pessoa, I. McGlynn, Meagan Friedrichs, Americo L. Fernandez, J. Mathieu, Matthieu Vinchon, A. Onaisi
For tight reservoirs where hydraulic fracturing is required to enable sufficient fluid mobility for economic production, it is critical to understand the placement of induced fractures, their connectivity, extent, and interaction with natural fractures within the system. Hydraulic fracture initiation and propagation mechanisms are greatly influenced by the effect of the stress state, rock fabric and pre-existing features (e.g. natural fractures, faults, weak bedding/laminations). A pre-existing natural fracture system can dictate the mode, orientation and size of the hydraulic fracture network. A better understanding of the fracture growth phenomena will enhance productivity and also reduce the environmental footprint as less fractures can be created in a much more efficient way. Assessing the role of natural fractures and their interaction with hydraulic fractures in order to account for them in the hydraulic fracture model is achieved by leveraging microseismicity. In this study, we have used a combination of borehole and surface microseismic monitoring to get high vertical resolution locations and source mechanisms. 3D numerical modelling of hydraulic fracturing in complex geological conditions to predict fracture propagation is essential. 3D hydraulic fracturing simulation includes modelling capabilities of stimulation parameters, true 3D fracture propagation with near wellbore 3D complexity including a coupled DFN and the associated microseismic event generation capability. A 3D hydraulic fracture model was developed and validated by matching model predictions to microseismic observations. Microseismic source mechanisms are leveraged to determine the location and geometry of pre-existing features. In this study, we simulate a DFN based on the recorded seismicity of multi stage hydraulic fractures in a horizontal well. The advanced 3D hydraulic fracture modelling software can integrate effectively and efficiently data from a variety of multi-disciplinary sources and scales to create a subsurface characterization of the unconventional reservoir. By incorporating data from 3D seismic, LWD/wireline, core, completion/stimulation monitoring, and production, the software generates a holistic reservoir model embedded in a modular, multi-physics software platform of coupled numerical solvers that capture the fundamental physics of the processes being modelled. This study illustrates the importance of a powerful software tool that captures the necessary physics of stimulation to predict the effects of various completion designs and thereby ensure the most accurate representation of an unconventional reservoir response to a stimulation treatment.
{"title":"A Case Study Using Integrated Multi Disciplinary Approach to Model Microseismic Events During Stimulation","authors":"G. Izadi, C. Barton, P. Roux, Tebis Llobet, T. Pessoa, I. McGlynn, Meagan Friedrichs, Americo L. Fernandez, J. Mathieu, Matthieu Vinchon, A. Onaisi","doi":"10.2118/204856-ms","DOIUrl":"https://doi.org/10.2118/204856-ms","url":null,"abstract":"\u0000 For tight reservoirs where hydraulic fracturing is required to enable sufficient fluid mobility for economic production, it is critical to understand the placement of induced fractures, their connectivity, extent, and interaction with natural fractures within the system. Hydraulic fracture initiation and propagation mechanisms are greatly influenced by the effect of the stress state, rock fabric and pre-existing features (e.g. natural fractures, faults, weak bedding/laminations). A pre-existing natural fracture system can dictate the mode, orientation and size of the hydraulic fracture network. A better understanding of the fracture growth phenomena will enhance productivity and also reduce the environmental footprint as less fractures can be created in a much more efficient way. Assessing the role of natural fractures and their interaction with hydraulic fractures in order to account for them in the hydraulic fracture model is achieved by leveraging microseismicity. In this study, we have used a combination of borehole and surface microseismic monitoring to get high vertical resolution locations and source mechanisms. 3D numerical modelling of hydraulic fracturing in complex geological conditions to predict fracture propagation is essential. 3D hydraulic fracturing simulation includes modelling capabilities of stimulation parameters, true 3D fracture propagation with near wellbore 3D complexity including a coupled DFN and the associated microseismic event generation capability. A 3D hydraulic fracture model was developed and validated by matching model predictions to microseismic observations. Microseismic source mechanisms are leveraged to determine the location and geometry of pre-existing features. In this study, we simulate a DFN based on the recorded seismicity of multi stage hydraulic fractures in a horizontal well. The advanced 3D hydraulic fracture modelling software can integrate effectively and efficiently data from a variety of multi-disciplinary sources and scales to create a subsurface characterization of the unconventional reservoir. By incorporating data from 3D seismic, LWD/wireline, core, completion/stimulation monitoring, and production, the software generates a holistic reservoir model embedded in a modular, multi-physics software platform of coupled numerical solvers that capture the fundamental physics of the processes being modelled. This study illustrates the importance of a powerful software tool that captures the necessary physics of stimulation to predict the effects of various completion designs and thereby ensure the most accurate representation of an unconventional reservoir response to a stimulation treatment.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75173594","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. Al-Nakhli, Amjed Hassan, M. Mahmoud, Abdualilah Al-Baiz, Wajdi Buhaezah
Condensate banking represent a persistent challenge during gas production from tight reservoir. The accumulation of condensate around the wellbore can rapidly diminish gas production. When reservoir pressure drop below dew point, condensate start to dropout from gas phase, filling pores and permeable fractures, and block gas production. There are several strategies to mitigate condensate banking, however, these strategies are either demonstrate limited results or are economically not viable. In this study, a novel method to mitigate condensate was developed using thermochemical reactants. Slow-release of thermochemical reactants inside different core samples was studied. The effect of in-situ generation of gas on the petrophysical properties of the rock was reported. Thermochemical treatment was applied to recover condensate on sandstone and carbonate, where the reported recoveries were around 70%. However, when shale sample was used, the recovery was only 43%. Advanced Equation-of-State (EoS) compositional and unconventional simulator (GEM) from CMG (Computer Modelling Group) software was used to simulate thermochemical treatment and gas injection. The simulation study showed that thermochemical stimulation had increased production period from 3.5 to 22.7 months, compared to gas injection.
{"title":"Condensate Banking Removal Using Slow Release of In-Situ Energized Fluid","authors":"A. Al-Nakhli, Amjed Hassan, M. Mahmoud, Abdualilah Al-Baiz, Wajdi Buhaezah","doi":"10.2118/204729-ms","DOIUrl":"https://doi.org/10.2118/204729-ms","url":null,"abstract":"\u0000 Condensate banking represent a persistent challenge during gas production from tight reservoir. The accumulation of condensate around the wellbore can rapidly diminish gas production. When reservoir pressure drop below dew point, condensate start to dropout from gas phase, filling pores and permeable fractures, and block gas production. There are several strategies to mitigate condensate banking, however, these strategies are either demonstrate limited results or are economically not viable.\u0000 In this study, a novel method to mitigate condensate was developed using thermochemical reactants. Slow-release of thermochemical reactants inside different core samples was studied. The effect of in-situ generation of gas on the petrophysical properties of the rock was reported. Thermochemical treatment was applied to recover condensate on sandstone and carbonate, where the reported recoveries were around 70%. However, when shale sample was used, the recovery was only 43%.\u0000 Advanced Equation-of-State (EoS) compositional and unconventional simulator (GEM) from CMG (Computer Modelling Group) software was used to simulate thermochemical treatment and gas injection. The simulation study showed that thermochemical stimulation had increased production period from 3.5 to 22.7 months, compared to gas injection.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73048395","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}
CO2 has some critical technical and economic reasons for its use as an injection gas for oil recovery. CO2 is very soluble in crude oil at reservoir pressures; it contributes to sweep efficiency enhancement as it swells the oil and significantly reduces its viscosity. Although the mechanism of CO2 flooding is the same as that for other gases, CO2 is easier to handle, it is cheaper, and it is an environmentally better candidate than other gases. Formation evaluation and reservoir engineering have been major areas in the oil and gas industry that are heavily influenced by technology advances, to increase efficiency, improve hydrocarbon recovery and allow real-time reservoir monitoring. Water flooding for increasing oil recovery has been amongst the oldest production mechanisms widely utilized since the end of the 19th century to maintain pressure levels in the reservoir and push hydrocarbons accumulations towards the producing wellbore locations (Satter, Iqbal, & Buchwalter, 2008). Produced water from the reservoir formation was reinjected in order to maintain pressure levels, as well as seawater and aquifer water injection have also taken a strong mandate. With the advent of technology and processing plants this injection process was further refined, allowing salinity control of the injected water as well as monitor the injection and distribution of the water levels in near real time (Boussa, Bencherif, Hamza, & Khodja, 2005). Formation evaluation has seen an even greater penetration of technology in its area with the quest to achieve real-time formation evaluation during the drilling process. Conventional formation evaluation is conducted utilizing wireline logging technology, which is deployed after the drilling of the well and allows to analyze the reservoir formation. Given the significant advancement of logging technologies, acquiring the measurements during the drilling process (LWD) has been at the forefront of interest, allowing improved well placement and geosteering as well as real-time formation evaluation to optimize well completion strategies (Hill, 2017). Amongst the technologies recently deployed, surfaced logging and advanced mud and logging allow to determine on cuttings in real time mostly any of the properties previously possible only on direct measurements on cores (Santarelli, Marsala, Brignoli, Rossi, & Bona, 1998; Katterbauer & Marsala, A Novel Sparsity Deploying Reinforcement Deep Learning Algorithm for Saturation Mapping of Oil and Gas Reservoirs, 2021; Katterbauer, Marsala, Schoepf, & Donzier, 2021). With advances in AI, reservoir characterization is now moving towards real-time or near real-time analysis at the rig site. For near real-time analysis, the main physical source of data is drill cuttings as it guides the drilling operation by determining important depth point such as formation tops, coring intervals. Traditionally, the description of these cuttings is done manually by geologists at the well site. The acc
{"title":"A Deep Learning Wag Injection Method for Co2 Recovery Optimization","authors":"Klemens Katterbauer, A. Marsala, A. Qasim","doi":"10.2118/204711-ms","DOIUrl":"https://doi.org/10.2118/204711-ms","url":null,"abstract":"\u0000 CO2 has some critical technical and economic reasons for its use as an injection gas for oil recovery. CO2 is very soluble in crude oil at reservoir pressures; it contributes to sweep efficiency enhancement as it swells the oil and significantly reduces its viscosity. Although the mechanism of CO2 flooding is the same as that for other gases, CO2 is easier to handle, it is cheaper, and it is an environmentally better candidate than other gases.\u0000 Formation evaluation and reservoir engineering have been major areas in the oil and gas industry that are heavily influenced by technology advances, to increase efficiency, improve hydrocarbon recovery and allow real-time reservoir monitoring. Water flooding for increasing oil recovery has been amongst the oldest production mechanisms widely utilized since the end of the 19th century to maintain pressure levels in the reservoir and push hydrocarbons accumulations towards the producing wellbore locations (Satter, Iqbal, & Buchwalter, 2008). Produced water from the reservoir formation was reinjected in order to maintain pressure levels, as well as seawater and aquifer water injection have also taken a strong mandate. With the advent of technology and processing plants this injection process was further refined, allowing salinity control of the injected water as well as monitor the injection and distribution of the water levels in near real time (Boussa, Bencherif, Hamza, & Khodja, 2005).\u0000 Formation evaluation has seen an even greater penetration of technology in its area with the quest to achieve real-time formation evaluation during the drilling process. Conventional formation evaluation is conducted utilizing wireline logging technology, which is deployed after the drilling of the well and allows to analyze the reservoir formation. Given the significant advancement of logging technologies, acquiring the measurements during the drilling process (LWD) has been at the forefront of interest, allowing improved well placement and geosteering as well as real-time formation evaluation to optimize well completion strategies (Hill, 2017). Amongst the technologies recently deployed, surfaced logging and advanced mud and logging allow to determine on cuttings in real time mostly any of the properties previously possible only on direct measurements on cores (Santarelli, Marsala, Brignoli, Rossi, & Bona, 1998; Katterbauer & Marsala, A Novel Sparsity Deploying Reinforcement Deep Learning Algorithm for Saturation Mapping of Oil and Gas Reservoirs, 2021; Katterbauer, Marsala, Schoepf, & Donzier, 2021).\u0000 With advances in AI, reservoir characterization is now moving towards real-time or near real-time analysis at the rig site. For near real-time analysis, the main physical source of data is drill cuttings as it guides the drilling operation by determining important depth point such as formation tops, coring intervals. Traditionally, the description of these cuttings is done manually by geologists at the well site. The acc","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84806468","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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