Nanoparticles or nanocomposite fluids are injected into oil reservoirs for reservoir tracing or to improve injectivity or recovery of oil. Effective application of nanoparticles in fluid flooding still needs to be investigated. Dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles have been developed as nanoparticle reservoir tracers. This presentation discusses their transport and detectability in porous media, providing valuable information for understanding the role of nanoparticles in EOR process. The dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles are synthesized composed of Ag or Au metal cores, specific dye molecules, and a SiO2 shell materials. To optimize maximum signal enhancement of both phenomena such as SERS and SEF, the distance between core metal nanoparticles and dye molecules are precisely controlled. The synthesized composite nanoparticles barcoded with dye molecules are detectable by both fluorescence and Raman spectroscopies due to the SERS-SEF phenomena. Both fluorescence and Raman microscopic images of dye embedded surfaceenhanced Raman scattering (SERS) surface-enhanced fluorescence (SEF) composite nanoparticles in water phase successfully were collected within microfluidic reservoir-on-a-chip. The reservoir-on-a-chip utilized in this study fabricated based on reservoir rock geometry and coated with calcium carbonate. The synthesized SERS-SEF composite nanoparticles in water solution have been flooded into the microfluidic reservoir-on-a-chip and imaged for probing interfacial behavior of fluids such as liquid-liquid interfaces and studying the behavior of nanoparticles at liquid-rock interfaces. The precise synthesis method to produce the composite nanoparticles has been developed for the embedded dye molecules to generate noticeably enhanced detectability due to the strong SERS phenomenon. In conclusion, SERS-SEF nanoparticles barcoded with the fingerprinted Raman and fluorescence signals can provide a possible pathway toward SERS-SEF nanoprobe as various barcoded tracers to understand fluid behavior in porous media. Composite nanoparticle synthesis and its detection in flow technologies have been developed for visualization of the fluid flow behavior in porous media representing reservoir rock geometry. The results of the high-resolution nanoparticle fluid imaging data in reservoir-on-a-chip can be applied to understand mechanism of nanoparticle fluid assisted chemical enhanced oil recovery.
{"title":"Nanoparticle Tracers in Reservoir-On-A-chip by Surface-Enhanced Raman Scattering - Fluorescence SERS-SEF Imaging Technology","authors":"Sehoon Chang, S. Eichmann, W. Wang","doi":"10.2118/204704-ms","DOIUrl":"https://doi.org/10.2118/204704-ms","url":null,"abstract":"\u0000 Nanoparticles or nanocomposite fluids are injected into oil reservoirs for reservoir tracing or to improve injectivity or recovery of oil. Effective application of nanoparticles in fluid flooding still needs to be investigated. Dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles have been developed as nanoparticle reservoir tracers. This presentation discusses their transport and detectability in porous media, providing valuable information for understanding the role of nanoparticles in EOR process. The dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles are synthesized composed of Ag or Au metal cores, specific dye molecules, and a SiO2 shell materials. To optimize maximum signal enhancement of both phenomena such as SERS and SEF, the distance between core metal nanoparticles and dye molecules are precisely controlled. The synthesized composite nanoparticles barcoded with dye molecules are detectable by both fluorescence and Raman spectroscopies due to the SERS-SEF phenomena. Both fluorescence and Raman microscopic images of dye embedded surfaceenhanced Raman scattering (SERS) surface-enhanced fluorescence (SEF) composite nanoparticles in water phase successfully were collected within microfluidic reservoir-on-a-chip. The reservoir-on-a-chip utilized in this study fabricated based on reservoir rock geometry and coated with calcium carbonate. The synthesized SERS-SEF composite nanoparticles in water solution have been flooded into the microfluidic reservoir-on-a-chip and imaged for probing interfacial behavior of fluids such as liquid-liquid interfaces and studying the behavior of nanoparticles at liquid-rock interfaces. The precise synthesis method to produce the composite nanoparticles has been developed for the embedded dye molecules to generate noticeably enhanced detectability due to the strong SERS phenomenon. In conclusion, SERS-SEF nanoparticles barcoded with the fingerprinted Raman and fluorescence signals can provide a possible pathway toward SERS-SEF nanoprobe as various barcoded tracers to understand fluid behavior in porous media. Composite nanoparticle synthesis and its detection in flow technologies have been developed for visualization of the fluid flow behavior in porous media representing reservoir rock geometry. The results of the high-resolution nanoparticle fluid imaging data in reservoir-on-a-chip can be applied to understand mechanism of nanoparticle fluid assisted chemical enhanced oil recovery.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79207218","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}
As the oil and gas industry increases its focus on sustainability, including greenhouse gases emissions reductions and carbon footprint management, it is relevant to analyze optimal solutions integrating different renewable, green and hydrogen technologies into hybrid renewable energy systems and compare them with well gas-to-power approaches for off-grid, on-site power generation in upstream applications. This paper goes through a desk review of different types of upstream facilities and an overview of potential power requirements to consider for off-grid electrification. Then, different technologies used for off-grid hybrid renewable energy systems are introduced and compared in terms of potential uses and integration requirements. Furthermore, emission targets are presented along with potential economical constraints. With those aspects introduced, system sizing and assumptions are modeled, simulated and optimized. The different modeled cases, including integrated renewable energy systems and power-to-gas systems, are presented in terms of suitability in application to the facilities under consideration. For such cases, simulation results are presented in quantitative terms of equivalent optimized value for the multiple competing objectives in the study, in terms of sustainability targets and economics. Sensitivity analysis are also presented showing main parameters of influence on the optimal energy scheme approach. This paper provides a qualitative and quantitative analytical optimization approach evaluating multiple competing objectives in terms of green, renewable, hydrogen and gas-to-power technologies, economics and carbon footprint management for consideration in facilities power systems schemes.
{"title":"Multi-Objective Optimisation Analysis for Off-Grid, On-Site Power Generation Comparing Hybrid Renewable Energy Systems and Gas-to-Power Systems In Upstream Applications","authors":"S. A. Ruvalcaba Velarde","doi":"10.2118/204814-ms","DOIUrl":"https://doi.org/10.2118/204814-ms","url":null,"abstract":"\u0000 As the oil and gas industry increases its focus on sustainability, including greenhouse gases emissions reductions and carbon footprint management, it is relevant to analyze optimal solutions integrating different renewable, green and hydrogen technologies into hybrid renewable energy systems and compare them with well gas-to-power approaches for off-grid, on-site power generation in upstream applications.\u0000 This paper goes through a desk review of different types of upstream facilities and an overview of potential power requirements to consider for off-grid electrification. Then, different technologies used for off-grid hybrid renewable energy systems are introduced and compared in terms of potential uses and integration requirements. Furthermore, emission targets are presented along with potential economical constraints. With those aspects introduced, system sizing and assumptions are modeled, simulated and optimized.\u0000 The different modeled cases, including integrated renewable energy systems and power-to-gas systems, are presented in terms of suitability in application to the facilities under consideration. For such cases, simulation results are presented in quantitative terms of equivalent optimized value for the multiple competing objectives in the study, in terms of sustainability targets and economics. Sensitivity analysis are also presented showing main parameters of influence on the optimal energy scheme approach.\u0000 This paper provides a qualitative and quantitative analytical optimization approach evaluating multiple competing objectives in terms of green, renewable, hydrogen and gas-to-power technologies, economics and carbon footprint management for consideration in facilities power systems schemes.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81090995","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. Aljedaani, M. Alotaibi, S. Ayirala, A. Al-yousef
Many challenges and limitations are experienced while treating the produced water in oil fields, due to large volumes of water produced together with oil. In this paper, we propose a new method to treat produced water, by integrating humidification and de-humidification desalination (HDH) unit with waste heat, extracted from abandoned oil and gas wells. This solution is based on circulating the produced water through abandoned wells (both vertical and horizontal wells) and heat them up to 60-80°C so that the heated water can be directly used as hot feed water into the HDH unit. This eliminates either electricity or power requirements from an external source thereby significantly lowering the energy requirements. The direct use of hot produced water at the desired temperature range allows for better performance of the HDH desalination unit, while reducing the operating cost, besides minimizing CO2 emissions to the environment. The use of heat extracted from abandoned oil and gas wells in the form of geothermal energy enables the utilization of waste heat associated with existing wells, which is already available in most of the oil fields. The proposed method therefore provides a sustainable renewable energy solution for produced water desalination using HDH processes.
{"title":"An Eco-Friendly and Low Carbon Footprint Water Treatment Technology for Produced Water Recycling","authors":"A. Aljedaani, M. Alotaibi, S. Ayirala, A. Al-yousef","doi":"10.2118/204744-ms","DOIUrl":"https://doi.org/10.2118/204744-ms","url":null,"abstract":"\u0000 Many challenges and limitations are experienced while treating the produced water in oil fields, due to large volumes of water produced together with oil. In this paper, we propose a new method to treat produced water, by integrating humidification and de-humidification desalination (HDH) unit with waste heat, extracted from abandoned oil and gas wells. This solution is based on circulating the produced water through abandoned wells (both vertical and horizontal wells) and heat them up to 60-80°C so that the heated water can be directly used as hot feed water into the HDH unit. This eliminates either electricity or power requirements from an external source thereby significantly lowering the energy requirements. The direct use of hot produced water at the desired temperature range allows for better performance of the HDH desalination unit, while reducing the operating cost, besides minimizing CO2 emissions to the environment. The use of heat extracted from abandoned oil and gas wells in the form of geothermal energy enables the utilization of waste heat associated with existing wells, which is already available in most of the oil fields. The proposed method therefore provides a sustainable renewable energy solution for produced water desalination using HDH processes.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79339719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Oil&Gas industry has experienced three price crises over the past twelve years. Swings in the key variables of politics, economy and technology affect supply and demand dynamics and consequently oil prices. The rise of unconventional sources brought the industry into a recurrent surplus of supply, putting pressure on prices and the combination of a supply shock, shortage of storage and an unprecedent demand drop brought prices to a 30-years low in April 2020. Although volatile oil prices make it challenging for oil companies to manage their markets, the silver lining in low oil prices is that it forced the industry to focus on rendering their internal operations more efficient. O&G producers cut their costs dramatically to remain profitable. The industry embarked on an optimization path and consequently accelerated the adoption of digital transformation. The COVID-19 crisis along with increasing societal pressure has only been a catalyzer to this digital transformation, unlocking significant operational improvements and reducing carbon emissions. According to the latest Rystad Energy analysis average breakeven price dropped 35% between 2014 and 2018, and an additional 10% over the last 2 years, to a $50 breakeven price per barrel.
{"title":"How Digitization Lowers Oil & Gas Industry Break Even Cost","authors":"S. Dufour, ra Sharma","doi":"10.2118/204753-ms","DOIUrl":"https://doi.org/10.2118/204753-ms","url":null,"abstract":"The Oil&Gas industry has experienced three price crises over the past twelve years. Swings in the key variables of politics, economy and technology affect supply and demand dynamics and consequently oil prices. The rise of unconventional sources brought the industry into a recurrent surplus of supply, putting pressure on prices and the combination of a supply shock, shortage of storage and an unprecedent demand drop brought prices to a 30-years low in April 2020.\u0000 Although volatile oil prices make it challenging for oil companies to manage their markets, the silver lining in low oil prices is that it forced the industry to focus on rendering their internal operations more efficient. O&G producers cut their costs dramatically to remain profitable. The industry embarked on an optimization path and consequently accelerated the adoption of digital transformation. The COVID-19 crisis along with increasing societal pressure has only been a catalyzer to this digital transformation, unlocking significant operational improvements and reducing carbon emissions.\u0000 According to the latest Rystad Energy analysis average breakeven price dropped 35% between 2014 and 2018, and an additional 10% over the last 2 years, to a $50 breakeven price per barrel.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80120240","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}
Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction. Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses. For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates. Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis. This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction. This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.
{"title":"Deep Similarity Learning for Well Test Model Identification","authors":"G. Nagaraj, Prashanth Pillai, Mandar Kulkarni","doi":"10.2118/204675-ms","DOIUrl":"https://doi.org/10.2118/204675-ms","url":null,"abstract":"\u0000 Over the years, well test analysis or pressure transient analysis (PTA) methods have progressed from straight lines via type curve analysis to pressure derivatives and deconvolution methods. Today, analysis of the log-log (pressure and its derivative) response is the most used method for PTA. Although these methods are widely available through commercial software, they are not fully automated, and human interaction is needed for their application. Furthermore, PTA is described as an inverse problem, whose solution in general is non-unique, and several models (well, reservoir and boundary) can be found applicable to similar pressure-derivative response. This tends to always bring about confusion in choosing the correct model using the conventional approach. This results in multiple iterations that are time consuming and requires constant human interaction.\u0000 Our approach automates the process of PTA using a Siamese neural network (SNN) architecture comprised of Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) layers. The SNN model is trained on simulated experimental data created using a design of experiments (DOE) approach involving most common 14 interpretation scenarios across well, reservoir, and boundary model types. Across each model type, parameters such as permeability, horizontal well length, skin factor, and distance to the boundary were sampled to compute 560 different pressure derivative responses. SNN is trained using a self-supervised training strategy where the positive and negative pairs are generated from the training data. We use transformations such as compression and expansion to generate positive pairs and negative pairs for the well test model responses.\u0000 For a given well test model response, similarity scores are computed against the candidates in each model class, and the best match from each class is identified. These matches are then ranked according to the similarity scores to identify optimal candidates.\u0000 Experimental analysis indicated that the true model class frequently appeared among the top ranked classes. The model achieves an accuracy of 93% for the top one model recommendations when tested on 70 samples from the 14 interpretation scenarios. Prior information on the top ranked probable well test models, significantly reduces the manual effort involved in the analysis.\u0000 This machine learning (ML) approach can be integrated with any PTA software or function as a standalone application in the interpreter's system. Current work using SNN with LSTM layers can be used to speed up the process of detecting the pressure derivative response explained by a certain combination of well, reservoir and boundary models and produce models with less user interaction.\u0000 This methodology will facilitate the interpretation engineer in making the model recognition faster for detailed integration with additional information from sources such as geophysics, geology, petrophysics, drilling, and production logging.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75651945","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}
Meshal Al-Khaldi, Dhari Al-Saadi, Mohammad Al-Ajmi, Abhijit Dutta, Ibrahim Elafify, N. Farhi, W. Nouh
This project began when a 9-5/8" in 43.5 ppf production casing became inaccessible due to the existing cemented pipe inside, preventing further reservoir section exposure and necessitating a mechanical side-track meanwhile introducing the challenge of loosing one section and imposimg slim hole challenges. The size and weight of the double-casing made for challenging drilling, as did the eight very different formations, which were drilled. The side-track was accomplished in two steps, an 8½ in hole followed by a single long 6⅛ in section, rather than the three steps (16 in, 12¼ in, 8½ in) that are typically required. The optimal kick off point carfully located across the dual casing by running electromagnetic diagnostics, the casing collar locator, and the cement bond log. The double casing mill was carefully tailored to successfully accomplish the exit in one run. Moreover, an extra 26 ft. MD rathole was drilled, which helped to eliminate the mud motor elongation run. A rotary steerable system was utilized directly in a directional BHA to drill an 8½ in open hole building section from vertical to a 30⁰ inclination. A 7.0 in liner was then set to isolate weak zones at the equivalent depth of the outer casing (13-3/8"). Subsequently, a single 6⅛ in section was drilled to the well TD through the lower eight formations. Drilling a 6⅛ in section through eight formations came with a variety of challenges. These formations have different challenging behaviors relative to the wellbore pressure that typically leads to the drilling being done in two sections. Modeling the geo-mechanical characteristics of each formation allowed the determination of a mud weight range and rheology that would stabilize the wellbore through all eight formations. The slim, 6⅛ in, hole was stabilized with higher equivalent circulating density (ECD) values than is typically used in larger boreholes. Optimizing mud weight and drilling parameters, while managing differential sticking with close monitoring of real-time ECD, helped to stabilize the high-pressurized zones to deliver the well to the desired TD with a single borehole. This project represents the first time in Kuwait that double casings in such large sizes have been cut and sidetracked. It is also the first time these eight formations have been cut across such a smaller hole size, slim hole (6⅛ in) in a single shot. Geo-mechanical modeling allowed us to stabilize the pressurized formations and to control the ECD. The well also deployed the longest production liner in the field commingling multiple reservoirs with differnt pore pressure ramps, with excellent cement quality providing optimal zonal isolation.
{"title":"Double Casing Exit for Side-Track to Commingle Two Borehole Sizes Based Sections in One Slim Shot to Well TD","authors":"Meshal Al-Khaldi, Dhari Al-Saadi, Mohammad Al-Ajmi, Abhijit Dutta, Ibrahim Elafify, N. Farhi, W. Nouh","doi":"10.2118/204881-ms","DOIUrl":"https://doi.org/10.2118/204881-ms","url":null,"abstract":"\u0000 This project began when a 9-5/8\" in 43.5 ppf production casing became inaccessible due to the existing cemented pipe inside, preventing further reservoir section exposure and necessitating a mechanical side-track meanwhile introducing the challenge of loosing one section and imposimg slim hole challenges. The size and weight of the double-casing made for challenging drilling, as did the eight very different formations, which were drilled. The side-track was accomplished in two steps, an 8½ in hole followed by a single long 6⅛ in section, rather than the three steps (16 in, 12¼ in, 8½ in) that are typically required.\u0000 The optimal kick off point carfully located across the dual casing by running electromagnetic diagnostics, the casing collar locator, and the cement bond log. The double casing mill was carefully tailored to successfully accomplish the exit in one run. Moreover, an extra 26 ft. MD rathole was drilled, which helped to eliminate the mud motor elongation run. A rotary steerable system was utilized directly in a directional BHA to drill an 8½ in open hole building section from vertical to a 30⁰ inclination. A 7.0 in liner was then set to isolate weak zones at the equivalent depth of the outer casing (13-3/8\"). Subsequently, a single 6⅛ in section was drilled to the well TD through the lower eight formations.\u0000 Drilling a 6⅛ in section through eight formations came with a variety of challenges. These formations have different challenging behaviors relative to the wellbore pressure that typically leads to the drilling being done in two sections. Modeling the geo-mechanical characteristics of each formation allowed the determination of a mud weight range and rheology that would stabilize the wellbore through all eight formations. The slim, 6⅛ in, hole was stabilized with higher equivalent circulating density (ECD) values than is typically used in larger boreholes. Optimizing mud weight and drilling parameters, while managing differential sticking with close monitoring of real-time ECD, helped to stabilize the high-pressurized zones to deliver the well to the desired TD with a single borehole.\u0000 This project represents the first time in Kuwait that double casings in such large sizes have been cut and sidetracked. It is also the first time these eight formations have been cut across such a smaller hole size, slim hole (6⅛ in) in a single shot. Geo-mechanical modeling allowed us to stabilize the pressurized formations and to control the ECD. The well also deployed the longest production liner in the field commingling multiple reservoirs with differnt pore pressure ramps, with excellent cement quality providing optimal zonal isolation.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87455812","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. Hoang, Tung Tran, Tan Nguyen, T. Truong, Duy Pham, T. Tran, Vinh X. Trinh, A. Ngo
This paper reports a successful case study of applying machine learning to improve the history matching process, making it easier, less time-consuming, and more accurate, by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs as well as determining the required LGR setup to history match those gas-condensate producers. History matching Hai Thach gas-condensate production wells is extremely challenging due to the combined effect of condensate banking, sub-seismic fault network, complex reservoir distribution and connectivity, uncertain HIIP, and lack of PVT data for most reservoirs. In fact, for some wells, many trial simulation runs were conducted before it became clear that LGR with transmissibility multiplier was required to obtain good history matching. In order to minimize this time-consuming trial-and-error process, machine learning was applied in this study to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the history matching process begins. Furthermore, machine learning application could also determine the required LGR setup. The method helped provide better models in a much shorter time, and greatly improved the efficiency and reliability of the dynamic modeling process. More than 500 synthetic samples were generated using compositional sector models and divided into separate training and test sets. Multiple classification algorithms such as logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, multinomial Naive Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as artificial neural networks were applied to predict whether LGR was used in the sector models. The best algorithm was found to be the Decision Tree classifier, with 100% accuracy on the training set and 99% accuracy on the test set. The LGR setup (size of LGR area and range of transmissibility multiplier) was also predicted best by the Decision Tree classifier with 91% accuracy on the training set and 88% accuracy on the test set. The machine learning model was validated using actual production data and the dynamic models of history-matched wells. Finally, using the machine learning prediction on wells with poor history matching results, their dynamic models were updated and significantly improved.
{"title":"Successful Case Study of Machine Learning Application to Streamline and Improve History Matching Process for Complex Gas-Condensate Reservoirs in Hai Thach Field, Offshore Vietnam","authors":"S. Hoang, Tung Tran, Tan Nguyen, T. Truong, Duy Pham, T. Tran, Vinh X. Trinh, A. Ngo","doi":"10.2118/204835-ms","DOIUrl":"https://doi.org/10.2118/204835-ms","url":null,"abstract":"\u0000 This paper reports a successful case study of applying machine learning to improve the history matching process, making it easier, less time-consuming, and more accurate, by determining whether Local Grid Refinement (LGR) with transmissibility multiplier is needed to history match gas-condensate wells producing from geologically complex reservoirs as well as determining the required LGR setup to history match those gas-condensate producers.\u0000 History matching Hai Thach gas-condensate production wells is extremely challenging due to the combined effect of condensate banking, sub-seismic fault network, complex reservoir distribution and connectivity, uncertain HIIP, and lack of PVT data for most reservoirs. In fact, for some wells, many trial simulation runs were conducted before it became clear that LGR with transmissibility multiplier was required to obtain good history matching. In order to minimize this time-consuming trial-and-error process, machine learning was applied in this study to analyze production data using synthetic samples generated by a very large number of compositional sector models so that the need for LGR could be identified before the history matching process begins. Furthermore, machine learning application could also determine the required LGR setup. The method helped provide better models in a much shorter time, and greatly improved the efficiency and reliability of the dynamic modeling process.\u0000 More than 500 synthetic samples were generated using compositional sector models and divided into separate training and test sets. Multiple classification algorithms such as logistic regression, Gaussian Naive Bayes, Bernoulli Naive Bayes, multinomial Naive Bayes, linear discriminant analysis, support vector machine, K-nearest neighbors, and Decision Tree as well as artificial neural networks were applied to predict whether LGR was used in the sector models. The best algorithm was found to be the Decision Tree classifier, with 100% accuracy on the training set and 99% accuracy on the test set. The LGR setup (size of LGR area and range of transmissibility multiplier) was also predicted best by the Decision Tree classifier with 91% accuracy on the training set and 88% accuracy on the test set. The machine learning model was validated using actual production data and the dynamic models of history-matched wells. Finally, using the machine learning prediction on wells with poor history matching results, their dynamic models were updated and significantly improved.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77482146","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}
Mauricio Espinosa, Jairo Leal, R. Zbitowsky, E. Pacheco
This paper highlights the first successful application of a field deployment of a high-temperature (HT) downhole shut-in tool (DHSIT) in multistage fracturing completions (MSF) producing retrograde gas condensate and from sour carbonate reservoirs. Many gas operators and service providers have made various attempts in the past to evaluate the long-term benefit of MSF completions while deploying DHSIT devices but have achieved only limited success (Ref. 1 and 2). During such deployments, many challenges and difficulties were faced in the attempt to deploy and retrieve those tools as well as to complete sound data interpretation to successfully identify both reservoir, stimulation, and downhole productivity parameters, and especially when having a combination of both heterogeneous rocks having retrograde gas pressure-volume-temperature (PVT) complexities. Therefore, a robust design of a DHSIT was needed to accurately shut-in the well, hold differential pressure, capture downhole pressure transient data, and thereby identify acid fracture design/conductivity, evaluate total KH, reduce wellbore storage effects, properly evaluate transient pressure effects, and then obtain a better understanding of frac geometry, reservoir parameters, and geologic uncertainties. Several aspects were taken into consideration for overcoming those challenges when preparing the DHSIT tool design including but not limited to proper metallurgy selection, enough gas flow area, impact on well drawdown, tool differential pressure, proper elastomer selection, shut-in time programming, internal completion diameter, and battery operation life and temperature. This paper is based on the first successful deployment and retrieval of the DHSIT in a 4-½" MSF sour carbonate gas well. The trial proved that all design considerations were important and took into consideration all well parameters. This project confirmed that DHSIT devices can successfully withstand the challenges of operating in sour carbonate MSF gas wells as well as minimize operational risk. This successful trial demonstrates the value of utilizing the DHSIT, and confirms more tangible values for wellbore conductivity post stimulation. All this was achieved by the proper metallurgy selection, maximizing gas flow area, minimizing the impact on well drawdown, and reducing well shut-in time and deferred gas production. Proper battery selection and elastomer design also enabled the tool to be operated at temperatures as high as 350 °F. The case study includes the detailed analysis of deployment and retrieval lessons learned, and includes equalization procedures, which added to the complexity of the operation. The paper captures all engineering concepts, tool design, setting packer mechanism, deployment procedures, and tool equalization and retrieval along with data evaluation and interpretation. In addition to lessons learned based on the field trial, various recommendations will be presented to minimize operational ri
{"title":"Openhole Multistage Completion Evaluation Incorporating Deployment of Downhole Shut-in Tool Application in Sour Carbonate Gas Wells, Field Application","authors":"Mauricio Espinosa, Jairo Leal, R. Zbitowsky, E. Pacheco","doi":"10.2118/204905-ms","DOIUrl":"https://doi.org/10.2118/204905-ms","url":null,"abstract":"\u0000 This paper highlights the first successful application of a field deployment of a high-temperature (HT) downhole shut-in tool (DHSIT) in multistage fracturing completions (MSF) producing retrograde gas condensate and from sour carbonate reservoirs. Many gas operators and service providers have made various attempts in the past to evaluate the long-term benefit of MSF completions while deploying DHSIT devices but have achieved only limited success (Ref. 1 and 2). During such deployments, many challenges and difficulties were faced in the attempt to deploy and retrieve those tools as well as to complete sound data interpretation to successfully identify both reservoir, stimulation, and downhole productivity parameters, and especially when having a combination of both heterogeneous rocks having retrograde gas pressure-volume-temperature (PVT) complexities.\u0000 Therefore, a robust design of a DHSIT was needed to accurately shut-in the well, hold differential pressure, capture downhole pressure transient data, and thereby identify acid fracture design/conductivity, evaluate total KH, reduce wellbore storage effects, properly evaluate transient pressure effects, and then obtain a better understanding of frac geometry, reservoir parameters, and geologic uncertainties. Several aspects were taken into consideration for overcoming those challenges when preparing the DHSIT tool design including but not limited to proper metallurgy selection, enough gas flow area, impact on well drawdown, tool differential pressure, proper elastomer selection, shut-in time programming, internal completion diameter, and battery operation life and temperature.\u0000 This paper is based on the first successful deployment and retrieval of the DHSIT in a 4-½\" MSF sour carbonate gas well. The trial proved that all design considerations were important and took into consideration all well parameters. This project confirmed that DHSIT devices can successfully withstand the challenges of operating in sour carbonate MSF gas wells as well as minimize operational risk. This successful trial demonstrates the value of utilizing the DHSIT, and confirms more tangible values for wellbore conductivity post stimulation. All this was achieved by the proper metallurgy selection, maximizing gas flow area, minimizing the impact on well drawdown, and reducing well shut-in time and deferred gas production. Proper battery selection and elastomer design also enabled the tool to be operated at temperatures as high as 350 °F. The case study includes the detailed analysis of deployment and retrieval lessons learned, and includes equalization procedures, which added to the complexity of the operation.\u0000 The paper captures all engineering concepts, tool design, setting packer mechanism, deployment procedures, and tool equalization and retrieval along with data evaluation and interpretation. In addition to lessons learned based on the field trial, various recommendations will be presented to minimize operational ri","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79163136","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}
We present a suite of numerical simulations of two-phase flow through a 2D model of a porous medium using the Rothman-Keller Lattice Boltzmann Method to study the effect of viscous fingering on the recovery factor as a function of viscosity ratio and wetting angle. This suite involves simulations spanning wetting angles from non-wetting to perfectly wetting and viscosity ratios spanning from 0.01 through 100. Each simulation is initialized with a porous model that is fully saturated with a "blue" fluid, and a "red" fluid is then injected from the left. The simulation parameters are set such that the capillary number is 10, well above the threshold for viscous fingering, and with a Reynolds number of 0.2 which is well below the transition to turbulence and small enough such that inertial effects are negligible. Each simulation involves the "red" fluid being injected from the left at a constant rate such in accord with the specified capillary number and Reynolds number until the red fluid breaks through the right side of the model. As expected, the dominant effect is the viscosity ratio, with narrow tendrils (viscous fingering) occurring for small viscosity ratios with M ≪ 1, and an almost linear front occurring for viscosity ratios above unity. The wetting angle is found to have a more subtle and complicated role. For low wetting angles (highly wetting injected fluids), the finger morphology is more rounded whereas for high wetting angles, the fingers become narrow. The effect of wettability on saturation (recovery factor) is more complex than the expected increase in recovery factor as the wetting angle is decreased, with specific wetting angles at certain viscosity ratios that optimize yield. This complex phase space landscape with hills, valleys and ridges suggests the dynamics of flow has a complex relationship with the geometry of the medium and hydrodynamical parameters, and hence recovery factors. This kind of behavior potentially has immense significance to Enhanced Oil Recovery (EOR). For the case of low viscosity ratio, the flow after breakthrough is localized mainly through narrow fingers but these evolve and broaden and the saturation continues to increase albeit at a reduced rate. For this reason, the recovery factor continues to increase after breakthrough and approaches over 90% after 10 times the breakthrough time.
{"title":"Study of the Effect of Wetting on Viscous Fingering Before and After Breakthrough by Lattice Boltzmann Simulations","authors":"P. Mora, G. Morra, Dave A. Yuen, R. Juanes","doi":"10.2118/204536-ms","DOIUrl":"https://doi.org/10.2118/204536-ms","url":null,"abstract":"\u0000 We present a suite of numerical simulations of two-phase flow through a 2D model of a porous medium using the Rothman-Keller Lattice Boltzmann Method to study the effect of viscous fingering on the recovery factor as a function of viscosity ratio and wetting angle. This suite involves simulations spanning wetting angles from non-wetting to perfectly wetting and viscosity ratios spanning from 0.01 through 100. Each simulation is initialized with a porous model that is fully saturated with a \"blue\" fluid, and a \"red\" fluid is then injected from the left. The simulation parameters are set such that the capillary number is 10, well above the threshold for viscous fingering, and with a Reynolds number of 0.2 which is well below the transition to turbulence and small enough such that inertial effects are negligible. Each simulation involves the \"red\" fluid being injected from the left at a constant rate such in accord with the specified capillary number and Reynolds number until the red fluid breaks through the right side of the model. As expected, the dominant effect is the viscosity ratio, with narrow tendrils (viscous fingering) occurring for small viscosity ratios with M ≪ 1, and an almost linear front occurring for viscosity ratios above unity. The wetting angle is found to have a more subtle and complicated role. For low wetting angles (highly wetting injected fluids), the finger morphology is more rounded whereas for high wetting angles, the fingers become narrow. The effect of wettability on saturation (recovery factor) is more complex than the expected increase in recovery factor as the wetting angle is decreased, with specific wetting angles at certain viscosity ratios that optimize yield. This complex phase space landscape with hills, valleys and ridges suggests the dynamics of flow has a complex relationship with the geometry of the medium and hydrodynamical parameters, and hence recovery factors. This kind of behavior potentially has immense significance to Enhanced Oil Recovery (EOR). For the case of low viscosity ratio, the flow after breakthrough is localized mainly through narrow fingers but these evolve and broaden and the saturation continues to increase albeit at a reduced rate. For this reason, the recovery factor continues to increase after breakthrough and approaches over 90% after 10 times the breakthrough time.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73891728","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}
Ruidong Zhao, Cai Wang, Hanjun Zhao, C. Xiong, Junfeng Shi, Xishun Zhang, Jinming Ren, Yonghui Zhang, Yizhen Sun
The conventional configurations of pumping well IOT consist of electric parameter indicator and dynamometer. The current, voltage, power, and other electrical parameters are easy to access, low costs, stable, and acquired daily during pumping well operation. If the working condition diagnosis and virtual production metering of pumping well can be realized through electrical parameters, the utilization of dynamometers can be cancelled or reduced, which is of great significance to reduce the investment and improve the coverage of IOT in oil wells. The conventional methods of diagnosis and analysis based on electrical parameters and virtual production metering are lack of theoretical basis. The combination of deep learning technology of big data and traditional methods will provide solutions to solve related technical problems. Considering that there are many energy transmission segments from the motor to the downhole pump, the characteristics of the electric parameter curve are more sophisticated and difficult to identify compared with dynamometer card due to the influence of the unbalance, pump fullness, rod/tube vibration, wax deposition and leakage. The shape characteristics of the electric parameter curve of the pumping well are analyzed in the time domain and frequency domain, which provides the basis for further diagnosis, analysis and production measurement. In this paper, an integrated multi-model diagnosis method is proposed. For the working conditions with a large scale of samples, the electrical parameters are converted to dynamometer cards for diagnosis by using the deep learning technology of big data. For the working conditions with sparse samples, the machine learning model is used to diagnosis directly with electrical parameters. The deep learning electric parameter model for production measurement is established. Through the combination of the big data model of electric parameters to dynamometer card, 3D mechanical model of rod string, and big data model of plunger leakage coefficient, the virtual production metering function of pumping well based on electrical parameters is successfully realized. The diagnosis and virtual production metering method and software based on electrical parameters have been applied in many oilfields of CNPC. The accuracy of identifying the upper and lower dead points of electric parameters is 98.0%; the coincidence rate of working condition diagnosis under electrical parameters is 92.0%; the average error of virtual production metering with electric parameters is 13.4%. The dynamometer and gauging room have been canceled in the demonstration area. The application of electrical parameters to diagnose working conditions and meter the production of pumping wells is the key to the low-cost IOT construction. Traditional mathematical and physical methods are difficult to solve this problem, but the application of big data analysis technology could do the job successfully.
{"title":"Research and Application of Rod Pump Working Condition Diagnosis and Virtual Production Metering Based on Electric Parameters","authors":"Ruidong Zhao, Cai Wang, Hanjun Zhao, C. Xiong, Junfeng Shi, Xishun Zhang, Jinming Ren, Yonghui Zhang, Yizhen Sun","doi":"10.2118/204785-ms","DOIUrl":"https://doi.org/10.2118/204785-ms","url":null,"abstract":"\u0000 The conventional configurations of pumping well IOT consist of electric parameter indicator and dynamometer. The current, voltage, power, and other electrical parameters are easy to access, low costs, stable, and acquired daily during pumping well operation. If the working condition diagnosis and virtual production metering of pumping well can be realized through electrical parameters, the utilization of dynamometers can be cancelled or reduced, which is of great significance to reduce the investment and improve the coverage of IOT in oil wells. The conventional methods of diagnosis and analysis based on electrical parameters and virtual production metering are lack of theoretical basis. The combination of deep learning technology of big data and traditional methods will provide solutions to solve related technical problems.\u0000 Considering that there are many energy transmission segments from the motor to the downhole pump, the characteristics of the electric parameter curve are more sophisticated and difficult to identify compared with dynamometer card due to the influence of the unbalance, pump fullness, rod/tube vibration, wax deposition and leakage. The shape characteristics of the electric parameter curve of the pumping well are analyzed in the time domain and frequency domain, which provides the basis for further diagnosis, analysis and production measurement. In this paper, an integrated multi-model diagnosis method is proposed. For the working conditions with a large scale of samples, the electrical parameters are converted to dynamometer cards for diagnosis by using the deep learning technology of big data. For the working conditions with sparse samples, the machine learning model is used to diagnosis directly with electrical parameters. The deep learning electric parameter model for production measurement is established. Through the combination of the big data model of electric parameters to dynamometer card, 3D mechanical model of rod string, and big data model of plunger leakage coefficient, the virtual production metering function of pumping well based on electrical parameters is successfully realized.\u0000 The diagnosis and virtual production metering method and software based on electrical parameters have been applied in many oilfields of CNPC. The accuracy of identifying the upper and lower dead points of electric parameters is 98.0%; the coincidence rate of working condition diagnosis under electrical parameters is 92.0%; the average error of virtual production metering with electric parameters is 13.4%. The dynamometer and gauging room have been canceled in the demonstration area.\u0000 The application of electrical parameters to diagnose working conditions and meter the production of pumping wells is the key to the low-cost IOT construction. Traditional mathematical and physical methods are difficult to solve this problem, but the application of big data analysis technology could do the job successfully.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82027319","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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