Enzyme can reduce interfacial tension between oil and water thereby mobilising more oil than would originally be produced but its adsorption on the porous rock surfaces reduces its efficiency. This study presents experimental investigation of dynamic adsorption of enzyme on sand surfaces. The experiment was carried out at varied brine salinities and enzyme concentrations on different sand grain sizes. The concentration depletion method that accounts for the difference in enzyme concentrations in solution before and after its contact with the sand was used to determine the enzyme adsorption on relevant surfaces. The effluent sample from the adsorption process was collected after every three minutes until equilibrium was reached and the final concentration of the enzyme in the effluent solutions was measured and used to determine its adsorbed concentration on the sand surfaces. The results of this study show that increase in concentration of enzyme results in increase in its adsorption on sand surfaces. Also, increase in brine salinity increased enzyme adsorption on the sand surfaces but increase in sand grain size however reduced its adsorption. The result of this study is relevant in the design of enzyme enhanced oil recovery process.
{"title":"Dynamic Adsorption of Enzyme on Sand Surfaces- An Experimental Study","authors":"Tinuola Udoh, Utibeabasi Benson","doi":"10.2118/211905-ms","DOIUrl":"https://doi.org/10.2118/211905-ms","url":null,"abstract":"\u0000 Enzyme can reduce interfacial tension between oil and water thereby mobilising more oil than would originally be produced but its adsorption on the porous rock surfaces reduces its efficiency. This study presents experimental investigation of dynamic adsorption of enzyme on sand surfaces. The experiment was carried out at varied brine salinities and enzyme concentrations on different sand grain sizes. The concentration depletion method that accounts for the difference in enzyme concentrations in solution before and after its contact with the sand was used to determine the enzyme adsorption on relevant surfaces. The effluent sample from the adsorption process was collected after every three minutes until equilibrium was reached and the final concentration of the enzyme in the effluent solutions was measured and used to determine its adsorbed concentration on the sand surfaces. The results of this study show that increase in concentration of enzyme results in increase in its adsorption on sand surfaces. Also, increase in brine salinity increased enzyme adsorption on the sand surfaces but increase in sand grain size however reduced its adsorption. The result of this study is relevant in the design of enzyme enhanced oil recovery process.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131509568","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}
Completion design is a very important phase in the development of a field, and it has evolved over the years with more sophisticated technology. One of the latest technology trends is the Intelligent Digital Oil fields, which although started decades ago has recently received rapid traction in past few years due to reduced costs and improvement in sensors and data storage. The starting point of this technology is the smart or intelligent well system (IWS). This system significantly improves reservoir management as it enables remote control and monitoring of downhole equipment’s. Consequently, this minimizes the need for any intervention and saves OPEX. However, the IWS has majorly being applied to a single string producer or injector. Previously, a single string completion is used across the multiple zones and hence must be commingled if the zones are to be produced simultaneously. The alternative to produce simultaneously without commingling is to use Dual string completions but they have a major drawback in that they always require some intervention to be done which is expensive. The aim of this project is therefore to test a novel idea of combining a Dual String completion with an Intelligent completion. A hypothetical field with three reservoir zones stacked was used for this study. The objective was to produce the lower zones through the long string as they had similar reservoir pressures and compatible fluids while the upper zone would produce through the short string as it is incompatible with the lower zones. Two cases were considered – with and without lower completions. The major difference in these cases was the position of the accessories. The adopted design uses a feed-through dual packer, two ICVs, and a dual gauge on the long string above the gravel pack packer and on the short string, a permanent gauge is placed above the feed-through dual packer and a total of 6 control lines is required. It is concluded that the design is feasible in both cases, and it solves the short comings of the dual string completions currently being used. The critical consideration is the well architecture. The knowledge from this paper serves as a foundation for what could become a new standard design for smart completions.
{"title":"A Novel Approach and Application to Dual String Design in Smart Well Completion","authors":"Daniel Omolewa, B. Oriji","doi":"10.2118/211997-ms","DOIUrl":"https://doi.org/10.2118/211997-ms","url":null,"abstract":"\u0000 Completion design is a very important phase in the development of a field, and it has evolved over the years with more sophisticated technology. One of the latest technology trends is the Intelligent Digital Oil fields, which although started decades ago has recently received rapid traction in past few years due to reduced costs and improvement in sensors and data storage. The starting point of this technology is the smart or intelligent well system (IWS). This system significantly improves reservoir management as it enables remote control and monitoring of downhole equipment’s. Consequently, this minimizes the need for any intervention and saves OPEX. However, the IWS has majorly being applied to a single string producer or injector. Previously, a single string completion is used across the multiple zones and hence must be commingled if the zones are to be produced simultaneously. The alternative to produce simultaneously without commingling is to use Dual string completions but they have a major drawback in that they always require some intervention to be done which is expensive. The aim of this project is therefore to test a novel idea of combining a Dual String completion with an Intelligent completion. A hypothetical field with three reservoir zones stacked was used for this study. The objective was to produce the lower zones through the long string as they had similar reservoir pressures and compatible fluids while the upper zone would produce through the short string as it is incompatible with the lower zones. Two cases were considered – with and without lower completions. The major difference in these cases was the position of the accessories. The adopted design uses a feed-through dual packer, two ICVs, and a dual gauge on the long string above the gravel pack packer and on the short string, a permanent gauge is placed above the feed-through dual packer and a total of 6 control lines is required. It is concluded that the design is feasible in both cases, and it solves the short comings of the dual string completions currently being used. The critical consideration is the well architecture. The knowledge from this paper serves as a foundation for what could become a new standard design for smart completions.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132919826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the concepts of environmentally sound technologies and sustainability. Environmentally sound technologies are potential ways capable of mitigating environmental pollution by adopting the use of energy efficient technologies. While sustainability is a process of change in which technological development and institutional change in which the exploitation of resource, the direction of investment, the orientation of technological development and institutional change are made consistent with future as well as present needs. In a broad sense sustainable development must enhance the long-term productivity of the resource base with acceptable environmental impacts. Using literature review and case studies of Britania U, a marginal oil field operator, Total Energy, and Shell Petroleum Development Company (SPDC). We find that environmentally sound technology can mitigate climate change. The study revealed that Britania used the technology which cleans out poisonous elements and emits smokeless air into the environment thereby mitigating climate change. Also, Total Energy, as part of its drive towards clean energy and reduce carbon emissions embarked on installation of solar energy while SPDC reported 17% decrease in routine flaring in 2020 due to the Southern Swamp Associated Gas Project which captured gas produced alongside oil in the Niger Delta. We find that environmentally sound technologies include all those technologies that reduce the negative impact of products and services on the natural environment. Furthermore, environmentally sound technologies have brought about increased opportunities for energy transition into cleaner forms of energy. We therefore recommend that developing countries try as much as possible to develop the internal capacities and embrace environmentally sound technologies to mitigate the negative consequences of climate change.
{"title":"Environmentally Sound Technologies for Sustainability and Climate Change in Niger Delta","authors":"H. Oruwari","doi":"10.2118/211933-ms","DOIUrl":"https://doi.org/10.2118/211933-ms","url":null,"abstract":"\u0000 This paper examines the concepts of environmentally sound technologies and sustainability. Environmentally sound technologies are potential ways capable of mitigating environmental pollution by adopting the use of energy efficient technologies. While sustainability is a process of change in which technological development and institutional change in which the exploitation of resource, the direction of investment, the orientation of technological development and institutional change are made consistent with future as well as present needs. In a broad sense sustainable development must enhance the long-term productivity of the resource base with acceptable environmental impacts. Using literature review and case studies of Britania U, a marginal oil field operator, Total Energy, and Shell Petroleum Development Company (SPDC). We find that environmentally sound technology can mitigate climate change. The study revealed that Britania used the technology which cleans out poisonous elements and emits smokeless air into the environment thereby mitigating climate change. Also, Total Energy, as part of its drive towards clean energy and reduce carbon emissions embarked on installation of solar energy while SPDC reported 17% decrease in routine flaring in 2020 due to the Southern Swamp Associated Gas Project which captured gas produced alongside oil in the Niger Delta. We find that environmentally sound technologies include all those technologies that reduce the negative impact of products and services on the natural environment. Furthermore, environmentally sound technologies have brought about increased opportunities for energy transition into cleaner forms of energy. We therefore recommend that developing countries try as much as possible to develop the internal capacities and embrace environmentally sound technologies to mitigate the negative consequences of climate change.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122925152","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}
E. Ekeinde, A. Dosunmu, D. C. Okujagu, Chigozie Agbawodikeizu
Electricity availability and adequate and efficient supply mechanisms are a huge driving force for a nation's economy and growth. Energy in all its forms (especially electricity) provide the bedrock through which a nation's industrial and technological advancement takes off and is sustained as processes depend on energy availability and utilization to ensure efficient delivery. The power sector reforms in Nigeria was done to reposition the power sector for increased productivity, but Nigerian power issues, vis-à-vis epileptic supply of electric power still persists. This study takes a look into the impediments to power revolution in the country with emphasis on the nation's power grid. It is seen that impediments like low generation capacities, insufficient transmission and distribution network, lack of adequate gas supply for gas-powered generating plants, insufficient investments in other forms of electricity generation like renewable energy, and improper government regulation still hinder reducing the huge gap between the electrical power needs of the population and the actual power generated and distributed. Recommendations are thereby made for further investigations to check restrictions to power generation and losses along the power transmission and distribution chain as well as improved generation and distribution from other energy sources in the country. Improvements in the transmission and distribution network systems to accommodate more power input is also advised.
{"title":"The Nigerian Power Grid and Impediments to Power Revolution in Nigeria","authors":"E. Ekeinde, A. Dosunmu, D. C. Okujagu, Chigozie Agbawodikeizu","doi":"10.2118/211931-ms","DOIUrl":"https://doi.org/10.2118/211931-ms","url":null,"abstract":"\u0000 Electricity availability and adequate and efficient supply mechanisms are a huge driving force for a nation's economy and growth. Energy in all its forms (especially electricity) provide the bedrock through which a nation's industrial and technological advancement takes off and is sustained as processes depend on energy availability and utilization to ensure efficient delivery. The power sector reforms in Nigeria was done to reposition the power sector for increased productivity, but Nigerian power issues, vis-à-vis epileptic supply of electric power still persists. This study takes a look into the impediments to power revolution in the country with emphasis on the nation's power grid. It is seen that impediments like low generation capacities, insufficient transmission and distribution network, lack of adequate gas supply for gas-powered generating plants, insufficient investments in other forms of electricity generation like renewable energy, and improper government regulation still hinder reducing the huge gap between the electrical power needs of the population and the actual power generated and distributed. Recommendations are thereby made for further investigations to check restrictions to power generation and losses along the power transmission and distribution chain as well as improved generation and distribution from other energy sources in the country. Improvements in the transmission and distribution network systems to accommodate more power input is also advised.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121847322","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 demand for cost-effective drilling operations in oil and gas exploration is ever growing. One of the important aspects to tackling the aforementioned difficulty is determining the optimal rate of penetration (ROP) of the drill bit. The most important optimization objective is to achieve a high optimal rate of penetration in safe and stable drilling conditions. Several machine learning models have been developed to predict ROP, however, there have been few studies that consider the different optimization algorithms needed to optimize the conventional developed models other than the conventional grid search and random search techniques. Genetic algorithm (GA) has gained much attention as methods of optimizing the predictions of machine learning algorithms in different fields of study. In this study, GA optimization algorithm was implemented to optimize 5 machine learning algorithms: Linear Regression, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron algorithm while using torque, weight on bit, surface RPM, mud flow, pump pressure, downhole temperature and pressure, etc, as input parameters. Three scenarios were analyzed using a train-test split ratio of 70-30, 80-20 and 85-15 percent on all the developed models. The results from the comparative study of all models developed shows that the implementation of the GA optimization algorithms increased the individual ROP models, with the multilayer perceptron model having the highest coefficient of determination of 0.989% after GA optimization.
{"title":"Application of Genetic Algorithm on Data Driven Models for Optimized ROP Prediction","authors":"David Duru, A. Kerunwa, J. Odo","doi":"10.2118/212016-ms","DOIUrl":"https://doi.org/10.2118/212016-ms","url":null,"abstract":"\u0000 The demand for cost-effective drilling operations in oil and gas exploration is ever growing. One of the important aspects to tackling the aforementioned difficulty is determining the optimal rate of penetration (ROP) of the drill bit. The most important optimization objective is to achieve a high optimal rate of penetration in safe and stable drilling conditions. Several machine learning models have been developed to predict ROP, however, there have been few studies that consider the different optimization algorithms needed to optimize the conventional developed models other than the conventional grid search and random search techniques. Genetic algorithm (GA) has gained much attention as methods of optimizing the predictions of machine learning algorithms in different fields of study. In this study, GA optimization algorithm was implemented to optimize 5 machine learning algorithms: Linear Regression, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron algorithm while using torque, weight on bit, surface RPM, mud flow, pump pressure, downhole temperature and pressure, etc, as input parameters. Three scenarios were analyzed using a train-test split ratio of 70-30, 80-20 and 85-15 percent on all the developed models. The results from the comparative study of all models developed shows that the implementation of the GA optimization algorithms increased the individual ROP models, with the multilayer perceptron model having the highest coefficient of determination of 0.989% after GA optimization.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122564868","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}
Managing well performance under reservoir uncertainty requires robust definition of the operating limits of the well. It also requires flexibility for reacting to observed performance trends and adjusting the well operating envelope to guarantee safe operations and stable production. BAJE-8 experienced anomaly in production performance occasioned by exposure of part of the drain hole to a gas pocket near the gas cap. The well definition carried uncertainty in fluid contact and there was also inability to map intra-reservoir faults and shales around the proposed landing depth of the drain hole. Uncertainty in the size of the gas pocket also led to the inability to accurately predict the timing of the blowdown. Prudent reservoir management enabled by proactive well monitoring and surveillance led to timely review and modification of operating conditions of the well when abnormally high GOR was observed from the well. The strategy was to blowdown the gas cap while monitoring changes in the well parameters with expectation that parameters would normalize once the gas pocket is blown down. The GOR and FTHP showed a peak in performance and steadily declined within the 6-month period of blow down. The blowdown pushed the well performance parameters towards the previously predicted ranges prompting modification of the operating envelope. This rollercoaster well performance occasioned by subsurface uncertainty lasted for some 6 months and was successfully managed. The well performance now aligns with predictions and the well has sustained production in the last 18 months.
{"title":"Managing Well Performance Under Reservoir Uncertainty: Case Study of a Niger Delta Well","authors":"E. Nnanna, Mofoluwake Nyeche","doi":"10.2118/211945-ms","DOIUrl":"https://doi.org/10.2118/211945-ms","url":null,"abstract":"\u0000 Managing well performance under reservoir uncertainty requires robust definition of the operating limits of the well. It also requires flexibility for reacting to observed performance trends and adjusting the well operating envelope to guarantee safe operations and stable production. BAJE-8 experienced anomaly in production performance occasioned by exposure of part of the drain hole to a gas pocket near the gas cap. The well definition carried uncertainty in fluid contact and there was also inability to map intra-reservoir faults and shales around the proposed landing depth of the drain hole. Uncertainty in the size of the gas pocket also led to the inability to accurately predict the timing of the blowdown. Prudent reservoir management enabled by proactive well monitoring and surveillance led to timely review and modification of operating conditions of the well when abnormally high GOR was observed from the well. The strategy was to blowdown the gas cap while monitoring changes in the well parameters with expectation that parameters would normalize once the gas pocket is blown down. The GOR and FTHP showed a peak in performance and steadily declined within the 6-month period of blow down.\u0000 The blowdown pushed the well performance parameters towards the previously predicted ranges prompting modification of the operating envelope. This rollercoaster well performance occasioned by subsurface uncertainty lasted for some 6 months and was successfully managed. The well performance now aligns with predictions and the well has sustained production in the last 18 months.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132733515","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}
J. Gbonhinbor, A. Obuebite, George Kuradoite, A. Agi
Chemical enhanced oil recovery (CEOR) application of natural surfactants is based on potential interfacial tension (IFT) alterability and eco-friendly considerations. The reduced IFT is associated with microemulsion formation in relation to a surfactant’s characteristic curvature. Lately, surface activities of natural surfactants have gained interest in Nigerian laboratory studies with no attention given to their hydrophilicity/hydrophobicity. This research focuses on molecular weight determination, micelle formation, and characteristic curvature evaluation of readily available natural surfactants. Four plants that are known to possess relevant surfactant properties were selected for this evaluation. Freezing point dipping method was used to determine the average molecular weight of each surfactant. Critical micelle concentration (CMC) was ascertained by electric conductivity tests. Characteristic curvature was evaluated from microemulsion formulations of toluene and aqueous surfactant mixtures. Formulated aqueous surfactant mixture consists of a combination of selected natural surfactant and a reference surfactant. Sodium dodecylsulphate (SDS) was adopted as the reference surfactant throughout this work. The analysis was configured in line with the hydrophilic-lipophilic deviation (HLD) model set to 0. Results yielded average molecular weights of examined surfactants between 128.3 g/mol to 186.7 g/mol. Critical micelle concentrations values of 0.45 to 0.60 were derived for all natural surfactants. Estimated characteristic curvature values suggested hydrophobicity with values from 0.116 to 0.194. As a consequence, these natural surfactants possess a tendency to form reverse micelles due oleic phase attraction. Their low positive values make them suitable for lowering IFT in order to mobilise trapped formation oil.
{"title":"Characteristic Curvature Assessment of Some Natural Surfactants for Chemical Enhanced Oil Recovery Applications in Nigeria","authors":"J. Gbonhinbor, A. Obuebite, George Kuradoite, A. Agi","doi":"10.2118/211996-ms","DOIUrl":"https://doi.org/10.2118/211996-ms","url":null,"abstract":"\u0000 Chemical enhanced oil recovery (CEOR) application of natural surfactants is based on potential interfacial tension (IFT) alterability and eco-friendly considerations. The reduced IFT is associated with microemulsion formation in relation to a surfactant’s characteristic curvature. Lately, surface activities of natural surfactants have gained interest in Nigerian laboratory studies with no attention given to their hydrophilicity/hydrophobicity. This research focuses on molecular weight determination, micelle formation, and characteristic curvature evaluation of readily available natural surfactants. Four plants that are known to possess relevant surfactant properties were selected for this evaluation. Freezing point dipping method was used to determine the average molecular weight of each surfactant. Critical micelle concentration (CMC) was ascertained by electric conductivity tests. Characteristic curvature was evaluated from microemulsion formulations of toluene and aqueous surfactant mixtures. Formulated aqueous surfactant mixture consists of a combination of selected natural surfactant and a reference surfactant. Sodium dodecylsulphate (SDS) was adopted as the reference surfactant throughout this work. The analysis was configured in line with the hydrophilic-lipophilic deviation (HLD) model set to 0. Results yielded average molecular weights of examined surfactants between 128.3 g/mol to 186.7 g/mol. Critical micelle concentrations values of 0.45 to 0.60 were derived for all natural surfactants. Estimated characteristic curvature values suggested hydrophobicity with values from 0.116 to 0.194. As a consequence, these natural surfactants possess a tendency to form reverse micelles due oleic phase attraction. Their low positive values make them suitable for lowering IFT in order to mobilise trapped formation oil.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114535323","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}
Oil price volatility is one of the major drivers, which drive the decision of Operators to drill oil wells to further develop oil fields. A more significant constraint, which deals a huge blow on the Marginal Field Operators in the Niger Delta is the huge and ‘unavailable’ CAPEX associated to the delivery of these wells. This paper elucidates how ‘detailed’ well design and optimization were used to design and deliver two swamp wells for a Marginal Field operator in the Niger Delta. With the application of detailed engineering and optimization processes, the well costs were reduced by over 50%. The wells were initially designed, and to be delivered for circa $13MM per well, which is the P50 cost of drilling Swamp wells in the Niger Delta. However, post design optimization, the wells were designed and delivered for circa 6.5MM per Well. The paper also details the drilling execution methods put in place to ensure that the wells designed were delivered efficiently.
{"title":"Cost Optimization by Designing an Ultra-Slim Horizontal Well in the Niger Delta – The Eremor Field Case Study","authors":"U. Okoli, H. Okwa, S. Adebayo, Ifiok Mkpong","doi":"10.2118/212041-ms","DOIUrl":"https://doi.org/10.2118/212041-ms","url":null,"abstract":"\u0000 Oil price volatility is one of the major drivers, which drive the decision of Operators to drill oil wells to further develop oil fields. A more significant constraint, which deals a huge blow on the Marginal Field Operators in the Niger Delta is the huge and ‘unavailable’ CAPEX associated to the delivery of these wells.\u0000 This paper elucidates how ‘detailed’ well design and optimization were used to design and deliver two swamp wells for a Marginal Field operator in the Niger Delta. With the application of detailed engineering and optimization processes, the well costs were reduced by over 50%.\u0000 The wells were initially designed, and to be delivered for circa $13MM per well, which is the P50 cost of drilling Swamp wells in the Niger Delta. However, post design optimization, the wells were designed and delivered for circa 6.5MM per Well.\u0000 The paper also details the drilling execution methods put in place to ensure that the wells designed were delivered efficiently.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114286645","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}
Calista Dikeh, C. Ikeokwu, T. Egbe, Murphy Nnamdi Ochuba, Moromoke Adekanye, Emmanuel G. Anifowose, E. Okoroafor
Subsurface numerical models take a significant time to build and run. For this reason, the energy industry has been looking towards proxy models that could reduce model computational time. With the advancement of artificial neural network algorithms, building proxy models has become more efficient, and has enabled quick forecasting and quick reservoir management decision-making. In this study, we used a geothermal reservoir to evaluate the suitability of two deep learning algorithms, feed forward neural network and convolutional neural network, for proxy modeling. We used metrics such as the mean square error, losses, number of parameters for the model, and time to run, to compare the two deep learning algorithms. From our study, we determined that the convolutional neural network resulted in less error than the feed forward network and used less hyperparameters. However, the feed forward network was significantly faster than the convolutional neural network. The process of building the proxy model shows how a similar approach can be followed for oil and gas reservoir modeling and demonstrates the feasibility of neural networks in subsurface reservoir modeling and forecasting.
{"title":"Artificial Neural Networks for Geothermal Reservoirs: Implications for Oil and Gas Reservoirs","authors":"Calista Dikeh, C. Ikeokwu, T. Egbe, Murphy Nnamdi Ochuba, Moromoke Adekanye, Emmanuel G. Anifowose, E. Okoroafor","doi":"10.2118/212028-ms","DOIUrl":"https://doi.org/10.2118/212028-ms","url":null,"abstract":"\u0000 Subsurface numerical models take a significant time to build and run. For this reason, the energy industry has been looking towards proxy models that could reduce model computational time. With the advancement of artificial neural network algorithms, building proxy models has become more efficient, and has enabled quick forecasting and quick reservoir management decision-making.\u0000 In this study, we used a geothermal reservoir to evaluate the suitability of two deep learning algorithms, feed forward neural network and convolutional neural network, for proxy modeling. We used metrics such as the mean square error, losses, number of parameters for the model, and time to run, to compare the two deep learning algorithms.\u0000 From our study, we determined that the convolutional neural network resulted in less error than the feed forward network and used less hyperparameters. However, the feed forward network was significantly faster than the convolutional neural network. The process of building the proxy model shows how a similar approach can be followed for oil and gas reservoir modeling and demonstrates the feasibility of neural networks in subsurface reservoir modeling and forecasting.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"589 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116312572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Odutola, Israel Bassey, Anita Igbine, Celestine Udim Monday
Advancements in oil and gas production have led to the exploration and production of hydrocarbons in unstable regions including offshore (deep & ultra-deep) reservoirs. As production increases, flow assurance continues to be a prevalent problem in wells and flowlines. It is necessary to develop flow assurance analysis models for hydrate formation in gas pipelines. Analyses have shown the difference in thermodynamic and kinetic behaviors in the different hydrate phase systems (water, gas, oil). This study presents a data-driven gas hydrate diagnosis model for monitoring and risk evaluation in gas pipelines by performing, hydrate growth rate diagnosis for flow assurance in gas-dominated flow systems. Data used for learning was obtained from hydrate flow loop experiments performed under controlled gas-dominated flow conditions where thermodynamic conditions were obtained at each time step. Regression Algorithms were applied to develop a fit for a model to predict the hydrate risk level given thermodynamic conditions alongside the flow rate. The developed hydrate model was also applied to study the performance in flow operations. The ridge regression model showed the best performance among the models with a root mean squared error of 0.1682 and a correlation coefficient of 0.9595. The results obtained showed that the model can be deployed for use in a hydrate risk analysis endeavor, and the algorithm used in development can be further improved.
{"title":"Hydrate Risk Management and Evaluation for Gas-Dominated Systems Using Machine Learning","authors":"T. Odutola, Israel Bassey, Anita Igbine, Celestine Udim Monday","doi":"10.2118/212000-ms","DOIUrl":"https://doi.org/10.2118/212000-ms","url":null,"abstract":"\u0000 Advancements in oil and gas production have led to the exploration and production of hydrocarbons in unstable regions including offshore (deep & ultra-deep) reservoirs. As production increases, flow assurance continues to be a prevalent problem in wells and flowlines.\u0000 It is necessary to develop flow assurance analysis models for hydrate formation in gas pipelines. Analyses have shown the difference in thermodynamic and kinetic behaviors in the different hydrate phase systems (water, gas, oil). This study presents a data-driven gas hydrate diagnosis model for monitoring and risk evaluation in gas pipelines by performing, hydrate growth rate diagnosis for flow assurance in gas-dominated flow systems. Data used for learning was obtained from hydrate flow loop experiments performed under controlled gas-dominated flow conditions where thermodynamic conditions were obtained at each time step. Regression Algorithms were applied to develop a fit for a model to predict the hydrate risk level given thermodynamic conditions alongside the flow rate. The developed hydrate model was also applied to study the performance in flow operations. The ridge regression model showed the best performance among the models with a root mean squared error of 0.1682 and a correlation coefficient of 0.9595. The results obtained showed that the model can be deployed for use in a hydrate risk analysis endeavor, and the algorithm used in development can be further improved.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114580283","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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