Electrical Submersible Pumps reliability and run-life analysis has been extensively studied since its development. Current machine learning algorithms allow to correlate operational conditions to ESP run-life in order to generate predictions for active and new wells. Four machine learning models are compared to a linear proportional hazards model, used as a baseline for comparison purposes. Proper accuracy metrics for survival analysis problems are calculated on run-life predictions vs. actual values over training and validation data subsets. Results demonstrate that the baseline model is able to produce more consistent predictions with a slight reduction in its accuracy, compared to current machine learning models for small datasets. This study demonstrates that the quality of the date and it pre-processing supports the current shift from model-centric to data-centric approach to machine and deep learning problems.
{"title":"Model Comparison for Esp Run-Life Prediction: Classic Statistics Vs. Machine Learning","authors":"Alejandro Celemín, Diego Estupiñan, Ricardo Nieto","doi":"10.2118/206028-ms","DOIUrl":"https://doi.org/10.2118/206028-ms","url":null,"abstract":"\u0000 Electrical Submersible Pumps reliability and run-life analysis has been extensively studied since its development. Current machine learning algorithms allow to correlate operational conditions to ESP run-life in order to generate predictions for active and new wells. Four machine learning models are compared to a linear proportional hazards model, used as a baseline for comparison purposes. Proper accuracy metrics for survival analysis problems are calculated on run-life predictions vs. actual values over training and validation data subsets. Results demonstrate that the baseline model is able to produce more consistent predictions with a slight reduction in its accuracy, compared to current machine learning models for small datasets. This study demonstrates that the quality of the date and it pre-processing supports the current shift from model-centric to data-centric approach to machine and deep learning problems.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86308133","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}
M. Pirrone, Satria Andrianata, S. Moriggi, G. Galli, S. Riva
Conventional downhole dynamic characterization is based on data from standard production logging tool (PLT) strings. Such method is not a feasible option in long horizontal drains, deep water scenarios, subsea clusters, pump-assisted wells and in presence of asphaltenes/solids deposition, mainly due to high costs and risk of tools stuck. In this respect, intrawell chemical tracers (ICT) can represent a valid and unobtrusive monitoring alternative. This paper deals with a new production allocation interpretation model of tracer concentration behavior that can overcome the limitation of standard PLT analyses in challenging environments. ICT are installed along the well completion and are characterized by a unique oil and/or water tracer signature at each selected production interval. Tracer concentration is obtained by dedicated analyses performed for each fluid sample taken at surface during transient production. Next, tracer concentration behavior over time is interpreted, for each producing interval, by means of an ad-hoc one-dimensional partial differential equation model with proper initial and boundary conditions, which describes tracer dispersion and advection profiles in such transient conditions. The full time-dependent analytical solutions are then utilized to obtain the final production allocation. The methodology has been developed and validated using data from a dozen of tracer campaigns. The approach is here presented through a selected case study, where a parallel acquisition of standard PLT and ICT data has been carried out in an offshore well. The aim was to understand if ICT could be used in substitution of the more impacting PLT for the future development wells in the field. At target, the well completion consists of a perforated production liner with tubing. The latter, which is slotted in front of the perforations, includes oil and water tracer systems. The straightforward PLT interpretation shows a clear dynamic well behavior with an oil production profile in line with the expectations from petrophysical information. Then, after a short shut-in period, the ICT-based production allocation has been performed in transient conditions with a very good match with the available outcomes from PLT: in fact, the maximum observed difference in the relative production rates is 5%. In addition, the full analytical solution of the ICT model has been fundamental to completely characterize some complex tracer concentration behaviors over time, corresponding to non-simultaneous activation of the different producing intervals. Given the consistency of the independent PLT and ICT interpretations, the monitoring campaign for the following years has been planned based on ICT only, with consequent impact on risk and cost mitigations. Although the added value of ICT is relatively well known, the successful description of the tracer signals through the full mathematical model is a novel topic and it can open the way for even more advanced applic
{"title":"Full Analytical Modeling Of Intrawell Chemical Tracer Concentration For Robust Production Allocation In Challenging Environments","authors":"M. Pirrone, Satria Andrianata, S. Moriggi, G. Galli, S. Riva","doi":"10.2118/206245-ms","DOIUrl":"https://doi.org/10.2118/206245-ms","url":null,"abstract":"\u0000 Conventional downhole dynamic characterization is based on data from standard production logging tool (PLT) strings. Such method is not a feasible option in long horizontal drains, deep water scenarios, subsea clusters, pump-assisted wells and in presence of asphaltenes/solids deposition, mainly due to high costs and risk of tools stuck. In this respect, intrawell chemical tracers (ICT) can represent a valid and unobtrusive monitoring alternative. This paper deals with a new production allocation interpretation model of tracer concentration behavior that can overcome the limitation of standard PLT analyses in challenging environments.\u0000 ICT are installed along the well completion and are characterized by a unique oil and/or water tracer signature at each selected production interval. Tracer concentration is obtained by dedicated analyses performed for each fluid sample taken at surface during transient production. Next, tracer concentration behavior over time is interpreted, for each producing interval, by means of an ad-hoc one-dimensional partial differential equation model with proper initial and boundary conditions, which describes tracer dispersion and advection profiles in such transient conditions. The full time-dependent analytical solutions are then utilized to obtain the final production allocation. The methodology has been developed and validated using data from a dozen of tracer campaigns.\u0000 The approach is here presented through a selected case study, where a parallel acquisition of standard PLT and ICT data has been carried out in an offshore well. The aim was to understand if ICT could be used in substitution of the more impacting PLT for the future development wells in the field. At target, the well completion consists of a perforated production liner with tubing. The latter, which is slotted in front of the perforations, includes oil and water tracer systems. The straightforward PLT interpretation shows a clear dynamic well behavior with an oil production profile in line with the expectations from petrophysical information. Then, after a short shut-in period, the ICT-based production allocation has been performed in transient conditions with a very good match with the available outcomes from PLT: in fact, the maximum observed difference in the relative production rates is 5%. In addition, the full analytical solution of the ICT model has been fundamental to completely characterize some complex tracer concentration behaviors over time, corresponding to non-simultaneous activation of the different producing intervals. Given the consistency of the independent PLT and ICT interpretations, the monitoring campaign for the following years has been planned based on ICT only, with consequent impact on risk and cost mitigations.\u0000 Although the added value of ICT is relatively well known, the successful description of the tracer signals through the full mathematical model is a novel topic and it can open the way for even more advanced applic","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90323983","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}
Hongfu Shi, Zhongbo Xu, H. Cai, Wenjun Zhang, Yunting Li
At present, the Bohai Oilfield has entered the late stage of high water cut, with a high degree of flooding and an average water cut of more than 80%. Horizontal wells were widely used in tapping the potentials of high water-cut oilfields with avoiding local water flooding, accurately develop enrichment of remaining oil, and improving initial productivity. Until 2020, there are more than 1,200 horizontal wells in the Bohai Oilfield, with daily production accounting for more than 40% of the entire oilfield. However, mainly continental deposits, strong heterogeneity, heavy oil, relatively large mobility ratio, long-term water flooding, and large liquid production have resulted in the obvious dominant channels in the formation, intensified ineffective water circulation, and low oil recovery. The application of horizontal wells faces huge challenges due to the serious water flooding and the prevalence of thief zones. Inflow Control Device (ICD) is becoming more and more prevalent in bottom water reservoirs as it can delay the water breakthrough and significantly improve the economic benefit of a project by producing more oil and less water. The strong microscopic heterogeneity along the horizontal water channeling outside the screen or water channeling along the annulus between the screen and ICD tubular is responsible for the short term even ineffective effect of conventional ICD. Based on the review of the conventional ICD application in the Q oilfield, a workflow is present to design and optimize hybrid ICD to increase the success probability of the validity period of water control.
{"title":"How to Block the Water Channels by High-Density Polyethylene Particles Supersaturated Filling Out-of-Screen and Inflow Control Device in Heterogeneous Sandstone Reservoir","authors":"Hongfu Shi, Zhongbo Xu, H. Cai, Wenjun Zhang, Yunting Li","doi":"10.2118/205952-ms","DOIUrl":"https://doi.org/10.2118/205952-ms","url":null,"abstract":"\u0000 At present, the Bohai Oilfield has entered the late stage of high water cut, with a high degree of flooding and an average water cut of more than 80%. Horizontal wells were widely used in tapping the potentials of high water-cut oilfields with avoiding local water flooding, accurately develop enrichment of remaining oil, and improving initial productivity. Until 2020, there are more than 1,200 horizontal wells in the Bohai Oilfield, with daily production accounting for more than 40% of the entire oilfield. However, mainly continental deposits, strong heterogeneity, heavy oil, relatively large mobility ratio, long-term water flooding, and large liquid production have resulted in the obvious dominant channels in the formation, intensified ineffective water circulation, and low oil recovery. The application of horizontal wells faces huge challenges due to the serious water flooding and the prevalence of thief zones.\u0000 Inflow Control Device (ICD) is becoming more and more prevalent in bottom water reservoirs as it can delay the water breakthrough and significantly improve the economic benefit of a project by producing more oil and less water. The strong microscopic heterogeneity along the horizontal water channeling outside the screen or water channeling along the annulus between the screen and ICD tubular is responsible for the short term even ineffective effect of conventional ICD. Based on the review of the conventional ICD application in the Q oilfield, a workflow is present to design and optimize hybrid ICD to increase the success probability of the validity period of water control.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90972022","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}
M. J. Ahsan, Shaikha Al-Turkey, N. Rane, F. Snasiri, A. Moustafa, H. Benyounes
The acquisition of mud gas data for well control and gathering of geological information is a common practice in oil and gas drilling. However, these data are scarcely used for reservoir evaluation as they are presumably considered as unreliable and non-representative of the formation content. Recent development in gas extraction from drilling mud and analyzing equipment has greatly improved the data quality. Combined with proper analysis and interpretation, these new datasets give valuable information in real-time lithological changes, hydrocarbons content, water contacts and vertical changes in fluid over a pay interval. Post completion, Mud logging data have been compared with PVT results and they have shown excellent correlation on the C1-C5 composition, confirming the consistency between gas readings and reservoir fluid composition. Having such information in real time has given the oil company the opportunity to optimize its operations regarding formation evaluation, e.g downhole sampling, wireline logging or testing programs. Formation fluid is usually obtained during well tests, either by running downhole tools into the well or by collecting the fluid at surface. Therefore, its composition remains unknown until the arrival of the PVT well test results. This case intends to use mud gas information collected while drilling to predict information about the reservoir fluid composition in near real time. To achieve this goal we compared mud gas data collected while drilling with reservoir fluid compositional results. Pressure volume temperature (PVT) analysis is the process of determining the fluid behaviors and properties of oil and gas samples from existing wells. The reason any oil and gas company decides to drill a well is to turn the project into an oil-producing asset. But the value of the oil extracted from a single well is not the same as the value of the oil produced from another. The makeup of the oil, which can be determined from the compositional analysis, is an important piece of the equation that determines how profitable the play will be. The compositional analysis will determine just how much of each type of petroleum product can be produced from a single barrel of oil from that wells. Formation samples were obtained from offset wells in the Marrat Formation. These datasets gave valuable indications on fluid properties and phase behavior in the reservoir and provided strong base for reservoir engineering analysis, simulation and surface facilities design. The comparison of the gas data to PVT results gives a good match for reservoir fluid finger print, early acquisition of this data will help for decision enhancement for field development.
{"title":"Advanced Gas While Drilling GWD Comparison with Pressure Volume Temperature PVT Analysis to Obtain Information About the Reservoir Fluid Composition, a Case Study from East Kuwait Jurassic Reservoir","authors":"M. J. Ahsan, Shaikha Al-Turkey, N. Rane, F. Snasiri, A. Moustafa, H. Benyounes","doi":"10.2118/206296-ms","DOIUrl":"https://doi.org/10.2118/206296-ms","url":null,"abstract":"\u0000 \u0000 \u0000 The acquisition of mud gas data for well control and gathering of geological information is a common practice in oil and gas drilling. However, these data are scarcely used for reservoir evaluation as they are presumably considered as unreliable and non-representative of the formation content.\u0000 Recent development in gas extraction from drilling mud and analyzing equipment has greatly improved the data quality. Combined with proper analysis and interpretation, these new datasets give valuable information in real-time lithological changes, hydrocarbons content, water contacts and vertical changes in fluid over a pay interval.\u0000 \u0000 \u0000 \u0000 Post completion, Mud logging data have been compared with PVT results and they have shown excellent correlation on the C1-C5 composition, confirming the consistency between gas readings and reservoir fluid composition. Having such information in real time has given the oil company the opportunity to optimize its operations regarding formation evaluation, e.g downhole sampling, wireline logging or testing programs.\u0000 Formation fluid is usually obtained during well tests, either by running downhole tools into the well or by collecting the fluid at surface. Therefore, its composition remains unknown until the arrival of the PVT well test results. This case intends to use mud gas information collected while drilling to predict information about the reservoir fluid composition in near real time. To achieve this goal we compared mud gas data collected while drilling with reservoir fluid compositional results.\u0000 Pressure volume temperature (PVT) analysis is the process of determining the fluid behaviors and properties of oil and gas samples from existing wells.\u0000 \u0000 \u0000 \u0000 The reason any oil and gas company decides to drill a well is to turn the project into an oil-producing asset. But the value of the oil extracted from a single well is not the same as the value of the oil produced from another. The makeup of the oil, which can be determined from the compositional analysis, is an important piece of the equation that determines how profitable the play will be. The compositional analysis will determine just how much of each type of petroleum product can be produced from a single barrel of oil from that wells.\u0000 \u0000 \u0000 \u0000 Formation samples were obtained from offset wells in the Marrat Formation. These datasets gave valuable indications on fluid properties and phase behavior in the reservoir and provided strong base for reservoir engineering analysis, simulation and surface facilities design.\u0000 The comparison of the gas data to PVT results gives a good match for reservoir fluid finger print, early acquisition of this data will help for decision enhancement for field development.\u0000","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87362217","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}
Rasha Al-Muraikhi, Nami Al-Mutairi, Karim Ousdidene, C. Magnier, Sachin Sharma, H. Benyounes
As the pursuit of oil and gas in Middle East Jurassic carbonates reservoirs grows, it is increasingly evident that horizontal wellbore placement, or targeting, plays a first-order role in the production capability of a well. Indeed, the percentage of a wellbore "in target" is a common metric used when evaluating the causes for good or poor production from any particular well. The most common process used for geosteering a horizontal wellbore into a chosen target is the correlation of logging-while-drilling (LWD) total gamma-ray (GR) to a vertical pilot-hole GR log or offset wells GR logs. However, limitations inherent to this procedure can reduce the ability to effectively use LWD GR data due to 4 ½" slim hole diameter and mud telemetry issues, the non-descript signal from LWD tools due to high pressure and high temperature and the possibility of lost signal from LWD tools. In addition, the thickness of MRW-F11 targeted reservoir is limited to plus or minus 22 ft and low GR contrast from bed to bed might lead to loss of directional control in the target MRW-F11. To accurately geosteer a well, Geochemical analyses of drilled cuttings are proposed to assist well placement. The analyses performed were elemental data derived from energy-dispersive X-ray fluorescence (ED-XRF) and mineralogical quantitative content acquired from the direct measurement from energy-dispersive X-ray Diffraction (ED-XRD). The Elemental and mineralogy data were acquired from drilling cuttings taken at ten feet intervals, from two offsets wells. The mineral and elemental data were used to build a chemo-stratigraphic profile and zonation of the sedimentary section. Chemo-stratigraphic zones are defined as having multiple elements and keys ratios (where possible) which illustrate distinct changes in chemical and mineralogical composition profiles from one zone to another. These zones were correlated over reasonable distances (at a minimum the length of the horizontal wellbore) and can be readily identifiable in cuttings. Using these criteria chemo-stratigraphic zonation's have been constructed in the Middle Marrat formation going from MRW-F1 toward MRW-F11 layer. Well site ED-XRF and ED-XRD data were used in conjunction with LWD Gamma Ray to geosteer at approximately 22 feet thin zone which resides at the base of an approximately 100 ft thick reservoir carbonate section of the main MRW-F11 reservoir. The LWD GR Signal was 45 ft behind the bit while all XRF and XRD data were at plus or minus 5 feet while sliding at plus or minus 10 ft in rotary mode and with a controlled slow rate of penetration (ROP) of 10 ft/hr. Geochemical rock analyses (GEAR) using XRF & XRD chemical analyses was the unique reference for approximately 500 ft interval to geosteer the well when LWD lost the signal, wiper trip was cancelled which considerably reduced drilling costs. Well site XRF and XRD data was successfully applied to geosteer the well, determine the position of the wellbore in zones
{"title":"Using XRF Elemental Data and XRD Direct Measured Mineralogy for an Accurate Wellbore Placement and Geosteering through Carbonates Reservoirs* Drilled Within 04 ½\" Slim Hole: A Case Study from a Jurassic Middle Marrat Carbonates Reservoir-Kuwait","authors":"Rasha Al-Muraikhi, Nami Al-Mutairi, Karim Ousdidene, C. Magnier, Sachin Sharma, H. Benyounes","doi":"10.2118/206328-ms","DOIUrl":"https://doi.org/10.2118/206328-ms","url":null,"abstract":"\u0000 As the pursuit of oil and gas in Middle East Jurassic carbonates reservoirs grows, it is increasingly evident that horizontal wellbore placement, or targeting, plays a first-order role in the production capability of a well. Indeed, the percentage of a wellbore \"in target\" is a common metric used when evaluating the causes for good or poor production from any particular well. The most common process used for geosteering a horizontal wellbore into a chosen target is the correlation of logging-while-drilling (LWD) total gamma-ray (GR) to a vertical pilot-hole GR log or offset wells GR logs. However, limitations inherent to this procedure can reduce the ability to effectively use LWD GR data due to 4 ½\" slim hole diameter and mud telemetry issues, the non-descript signal from LWD tools due to high pressure and high temperature and the possibility of lost signal from LWD tools. In addition, the thickness of MRW-F11 targeted reservoir is limited to plus or minus 22 ft and low GR contrast from bed to bed might lead to loss of directional control in the target MRW-F11.\u0000 To accurately geosteer a well, Geochemical analyses of drilled cuttings are proposed to assist well placement. The analyses performed were elemental data derived from energy-dispersive X-ray fluorescence (ED-XRF) and mineralogical quantitative content acquired from the direct measurement from energy-dispersive X-ray Diffraction (ED-XRD). The Elemental and mineralogy data were acquired from drilling cuttings taken at ten feet intervals, from two offsets wells.\u0000 The mineral and elemental data were used to build a chemo-stratigraphic profile and zonation of the sedimentary section. Chemo-stratigraphic zones are defined as having multiple elements and keys ratios (where possible) which illustrate distinct changes in chemical and mineralogical composition profiles from one zone to another. These zones were correlated over reasonable distances (at a minimum the length of the horizontal wellbore) and can be readily identifiable in cuttings. Using these criteria chemo-stratigraphic zonation's have been constructed in the Middle Marrat formation going from MRW-F1 toward MRW-F11 layer.\u0000 Well site ED-XRF and ED-XRD data were used in conjunction with LWD Gamma Ray to geosteer at approximately 22 feet thin zone which resides at the base of an approximately 100 ft thick reservoir carbonate section of the main MRW-F11 reservoir. The LWD GR Signal was 45 ft behind the bit while all XRF and XRD data were at plus or minus 5 feet while sliding at plus or minus 10 ft in rotary mode and with a controlled slow rate of penetration (ROP) of 10 ft/hr.\u0000 Geochemical rock analyses (GEAR) using XRF & XRD chemical analyses was the unique reference for approximately 500 ft interval to geosteer the well when LWD lost the signal, wiper trip was cancelled which considerably reduced drilling costs.\u0000 Well site XRF and XRD data was successfully applied to geosteer the well, determine the position of the wellbore in zones ","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87748653","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}
In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.
{"title":"Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks","authors":"A. Abubakar, Mandar Kulkarni, A. Kaul","doi":"10.2118/205985-ms","DOIUrl":"https://doi.org/10.2118/205985-ms","url":null,"abstract":"\u0000 In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82990744","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}
Recent papers on pre-frac tests have proposed fracture closure pressure interpretation methodologies that lead to an earlier, higher stress estimation than the ones estimated from well-established practices. These early time estimations based on the fracture compliance method lead the practitioner to utilize unrealistic permeability, stress, and fracture pressure models. This, in turn, has a severe impact on the modeled fracture geometries which hinders the hydraulic fracture optimization process. A multi-basin analysis of pre-frac tests from the North Sea, Europe, Russia, North Africa and South America is presented to support traditional closure estimation techniques. The validity of traditional minimum stress interpretation techniques will be reinforced through multiple case histories by comparing permeability estimates from the time required for the fracture to achieve closure during diagnostic injections, after-closure analysis, core, pressure build up and rate transient analysis. Results will be supported further by fiber optics and production logging tool (PLT) driven flow allocation, fracture geometry assessment through micro seismic and sonic anisotropy, and diagnostic injections numerical inversions.
{"title":"DFIT: An Interdisciplinary Validation of Fracture Closure Pressure Interpretation Across Multiple Basins","authors":"H. Buijs","doi":"10.2118/206239-ms","DOIUrl":"https://doi.org/10.2118/206239-ms","url":null,"abstract":"\u0000 Recent papers on pre-frac tests have proposed fracture closure pressure interpretation methodologies that lead to an earlier, higher stress estimation than the ones estimated from well-established practices. These early time estimations based on the fracture compliance method lead the practitioner to utilize unrealistic permeability, stress, and fracture pressure models. This, in turn, has a severe impact on the modeled fracture geometries which hinders the hydraulic fracture optimization process. A multi-basin analysis of pre-frac tests from the North Sea, Europe, Russia, North Africa and South America is presented to support traditional closure estimation techniques. The validity of traditional minimum stress interpretation techniques will be reinforced through multiple case histories by comparing permeability estimates from the time required for the fracture to achieve closure during diagnostic injections, after-closure analysis, core, pressure build up and rate transient analysis. Results will be supported further by fiber optics and production logging tool (PLT) driven flow allocation, fracture geometry assessment through micro seismic and sonic anisotropy, and diagnostic injections numerical inversions.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"21 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91554402","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}
O. H. Khan, Samad Ali, M. A. Elfeel, S. Biniwale, R. Dandekar
Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives. This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies. The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives. Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.
{"title":"Integrated Field Management System for LNG Assets: Maximizing Asset Value Through Representative End-To-End Modeling","authors":"O. H. Khan, Samad Ali, M. A. Elfeel, S. Biniwale, R. Dandekar","doi":"10.2118/205969-ms","DOIUrl":"https://doi.org/10.2118/205969-ms","url":null,"abstract":"\u0000 Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives.\u0000 This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies.\u0000 The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives.\u0000 Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91350536","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}
Xupeng He, Weiwei Zhu, R. Santoso, M. AlSinan, H. Kwak, H. Hoteit
Geologic CO2 Sequestration (GCS) is a promising engineering technology to reduce global greenhouse emissions. Real-time forecasting of CO2 leakage rates is an essential aspect of large-scale GCS deployment. This work introduces a data-driven, physics-featuring surrogate model based on deep-learning technique for CO2 leakage rate forecasting. The workflow for the development of data-driven, physics-featuring surrogate model includes three steps: 1) Datasets Generation: We first identify uncertainty parameters that affect the objective of interests (i.e., CO2 leakage rates). For the identified uncertainty parameters, various realizations are then generated based on Latin Hypercube Sampling (LHS). High-fidelity simulations based on a two-phase black-oil solver within MRST are performed to generate the objective functions. Datasets including inputs (i.e., the uncertainty parameters) and outputs (CO2 leakage rates) are collected. 2) Surrogate Development: In this step, a time-series surrogate model using long short-term memory (LSTM) is constructed to map the nonlinear relationship between these uncertainty parameters as inputs and CO2 leakage rates as outputs. We perform Bayesian optimization to automate the tuning of hyperparameters and network architecture instead of the traditional trial-error tuning process. 3) Uncertainty Analysis: This step aims to perform Monte Carlo (MC) simulations using the successfully trained surrogate model to explore uncertainty propagation. The sampled realizations are collected in the form of distributions from which the probabilistic forecast of percentiles, P10, P50, and P50, are evaluated. We propose a data-driven, physics-featuring surrogate model based on LSTM for CO2 leakage rate forecasting. We demonstrate its performance in terms of accuracy and efficiency by comparing it with ground-truth solutions. The proposed deep-learning workflow shows promising potential and could be readily implemented in commercial-scale GCS for real-time monitoring applications.
{"title":"CO2 Leakage Rate Forecasting Using Optimized Deep Learning","authors":"Xupeng He, Weiwei Zhu, R. Santoso, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2118/206222-ms","DOIUrl":"https://doi.org/10.2118/206222-ms","url":null,"abstract":"\u0000 Geologic CO2 Sequestration (GCS) is a promising engineering technology to reduce global greenhouse emissions. Real-time forecasting of CO2 leakage rates is an essential aspect of large-scale GCS deployment. This work introduces a data-driven, physics-featuring surrogate model based on deep-learning technique for CO2 leakage rate forecasting. The workflow for the development of data-driven, physics-featuring surrogate model includes three steps: 1) Datasets Generation: We first identify uncertainty parameters that affect the objective of interests (i.e., CO2 leakage rates). For the identified uncertainty parameters, various realizations are then generated based on Latin Hypercube Sampling (LHS). High-fidelity simulations based on a two-phase black-oil solver within MRST are performed to generate the objective functions. Datasets including inputs (i.e., the uncertainty parameters) and outputs (CO2 leakage rates) are collected. 2) Surrogate Development: In this step, a time-series surrogate model using long short-term memory (LSTM) is constructed to map the nonlinear relationship between these uncertainty parameters as inputs and CO2 leakage rates as outputs. We perform Bayesian optimization to automate the tuning of hyperparameters and network architecture instead of the traditional trial-error tuning process. 3) Uncertainty Analysis: This step aims to perform Monte Carlo (MC) simulations using the successfully trained surrogate model to explore uncertainty propagation. The sampled realizations are collected in the form of distributions from which the probabilistic forecast of percentiles, P10, P50, and P50, are evaluated. We propose a data-driven, physics-featuring surrogate model based on LSTM for CO2 leakage rate forecasting. We demonstrate its performance in terms of accuracy and efficiency by comparing it with ground-truth solutions. The proposed deep-learning workflow shows promising potential and could be readily implemented in commercial-scale GCS for real-time monitoring applications.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76002126","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}
New water treatment facilities in the Gulf of Mexico include a seawater Sulfate Removal Unit (SRU) to mitigate reservoir souring and scaling. The general industry sulfate target for offshore SRU is usually 20 mg/L or even 40 mg/L; however, some facilities may require <10 mg/L of sulfate in injection water, which makes water quality monitoring more critical and challenging. Current industrial practice relies on only pressure drop and a constant cleaning interval frequency to perform SRU maintenance which may result in reduced membrane life due to frequency cleaning or severe membrane fouling without the capability to predict fouling based on process conditions. The machine learning techniques applied will fill the gap and deliver a prediction model based on both simulation and real-time field data. This model will track and monitor the system key performance indicators (KPIs) including pressure, membrane fouling factor (FF), permeate sulfate concentration etc. The monitoring and prediction of these KPIs provide estimates on when the next maintenance procedure is required, track membrane system status for troubleshooting and actions, and optimize membrane performance by tuning operation conditions.
{"title":"Offshore Water Treatment KPIs Using Machine Learning Techniques","authors":"L. Flores, Martin Morles, Cheng Chen","doi":"10.2118/206173-ms","DOIUrl":"https://doi.org/10.2118/206173-ms","url":null,"abstract":"\u0000 New water treatment facilities in the Gulf of Mexico include a seawater Sulfate Removal Unit (SRU) to mitigate reservoir souring and scaling. The general industry sulfate target for offshore SRU is usually 20 mg/L or even 40 mg/L; however, some facilities may require <10 mg/L of sulfate in injection water, which makes water quality monitoring more critical and challenging. Current industrial practice relies on only pressure drop and a constant cleaning interval frequency to perform SRU maintenance which may result in reduced membrane life due to frequency cleaning or severe membrane fouling without the capability to predict fouling based on process conditions. The machine learning techniques applied will fill the gap and deliver a prediction model based on both simulation and real-time field data. This model will track and monitor the system key performance indicators (KPIs) including pressure, membrane fouling factor (FF), permeate sulfate concentration etc. The monitoring and prediction of these KPIs provide estimates on when the next maintenance procedure is required, track membrane system status for troubleshooting and actions, and optimize membrane performance by tuning operation conditions.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79662207","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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