J. Dala, Lateef T. Akanji, K. Bello, O. Olafuyi, Prashant Jadhawar
A new pseudo-radial pressure model for inflow performance analysis and near-wellbore condensate banking deliverability is developed. Analysis of condensate banking and evolution in near wellbore region (i.e. zone 3) has been extensively studied. The new zone 4 region identified in this work will help in delineating the limit of retrograde condensation and the onset of revapourisation. Revapourisation after retrograde condensation is usually not accounted for in most field applications. However, in mature fields such as the Oredo field investigated in this study, revapourisation and near wellbore dynamics play an important role in optimising production from the field. The results of the newly formulated model captured the transient retrograde revapourisation near the wellbore for the well X studied in this work.
{"title":"A Pseudo-Radial Pressure Model for Near-Wellbore Condensate Banking Prediction","authors":"J. Dala, Lateef T. Akanji, K. Bello, O. Olafuyi, Prashant Jadhawar","doi":"10.2118/208449-ms","DOIUrl":"https://doi.org/10.2118/208449-ms","url":null,"abstract":"\u0000 A new pseudo-radial pressure model for inflow performance analysis and near-wellbore condensate banking deliverability is developed. Analysis of condensate banking and evolution in near wellbore region (i.e. zone 3) has been extensively studied. The new zone 4 region identified in this work will help in delineating the limit of retrograde condensation and the onset of revapourisation. Revapourisation after retrograde condensation is usually not accounted for in most field applications. However, in mature fields such as the Oredo field investigated in this study, revapourisation and near wellbore dynamics play an important role in optimising production from the field. The results of the newly formulated model captured the transient retrograde revapourisation near the wellbore for the well X studied in this work.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75467812","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}
Each year, the Nigerian gas industry churns out big data on all channels of its value chain. The data is collated, analyzed, and reported by government agencies, corporate companies, institutions, and even academia. Some of these reports are the NNPC and DPR annual oil and gas reports. The annual oil and gas reports contain data tables, charts, and data driven insights. Considering the growing uncertainty in business intelligence triggered by the COVID-19 pandemic and the fast-paced 4th industrial revolution, the future of data reporting, analyzing, and presentation is also experiencing a new normal. Oil and gas stakeholders desire quick data-driven and actionable insights to reduce business risks caused by the impacts of these key drivers. This article explores and presents the use of Power BI on Nigerian gas data from 2000 to 2018. It extracts data on demand, production, utilization, gas flare volumes, export, current infrastructure capacity, domestic gas supply, and other relevant data categories. The collated data is developed into a dataset by appending and merging tables from the different reports. This data is prepared, and model relationships are created to answers questions on demand, production, infrastructure, and sustainability of the Nigerian Gas market. Empirical results show that new insights can be obtained from the dataset using new tools and a thoughtful data design process. These insights are presented on a dashboard where key takeaways for quick business decisions and policy implementations are easily assessed. The method is proposed as the future of annual energy reporting. It is also a continuous improvement process that can be applied by all oil and gas stakeholders in their data architecture.
{"title":"Data-Driven Insights from Nigeria's Natural Gas Data Using PowerBI","authors":"A. Adejola, O. Iledare, Paraclete Nnadili","doi":"10.2118/208238-ms","DOIUrl":"https://doi.org/10.2118/208238-ms","url":null,"abstract":"\u0000 Each year, the Nigerian gas industry churns out big data on all channels of its value chain. The data is collated, analyzed, and reported by government agencies, corporate companies, institutions, and even academia. Some of these reports are the NNPC and DPR annual oil and gas reports. The annual oil and gas reports contain data tables, charts, and data driven insights. Considering the growing uncertainty in business intelligence triggered by the COVID-19 pandemic and the fast-paced 4th industrial revolution, the future of data reporting, analyzing, and presentation is also experiencing a new normal. Oil and gas stakeholders desire quick data-driven and actionable insights to reduce business risks caused by the impacts of these key drivers. This article explores and presents the use of Power BI on Nigerian gas data from 2000 to 2018. It extracts data on demand, production, utilization, gas flare volumes, export, current infrastructure capacity, domestic gas supply, and other relevant data categories. The collated data is developed into a dataset by appending and merging tables from the different reports. This data is prepared, and model relationships are created to answers questions on demand, production, infrastructure, and sustainability of the Nigerian Gas market. Empirical results show that new insights can be obtained from the dataset using new tools and a thoughtful data design process. These insights are presented on a dashboard where key takeaways for quick business decisions and policy implementations are easily assessed. The method is proposed as the future of annual energy reporting. It is also a continuous improvement process that can be applied by all oil and gas stakeholders in their data architecture.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74647291","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}
Offshore oil and gas pipelines are subjected to high pressure and high temperature (HP/HT) from the inner hydrocarbon content during operation. Both the rise in temperature and internal pressure may cause longitudinal expansion of the pipeline. This expansion is restrained or semi-restrained by the pipe end devices and the soil which results in build-up of compression stresses in the pipe wall. These pipelines are also exposed to so many familiar and unfamiliar forces related to static, dynamic and environmental forces. This study presents a thorough review of various sources from literature on the integrity challenges of subsea flowlines and pipelines amid challenging operating conditions especially with regards to flow assurance. This paper evaluates the impact of hydrate deposition and agitation on the overall integrity of the subsea flowlines, riser-base and fitting e.g. elbows, valves e.t.c. A bow tie model was developed to determine the threats, causes, consequences, the top event and the impending hydrates that are to be designed and cause blockage and failure. Stress analysis were done with finite element tools which are ANSYS and Autodesk INVENTOR with only the hoop, Von Mises stress and the corresponding back pressure that occurred with the scenario of 0, 10,30,50,70,90 and 100% blockage of flowlines being analyzed and taking the 0% or null blockage as the pilot case with no hydrate formation. The result gotten from both results were validated with hand calculation with excel and the initial design values for the stress values before the initial operation of the wells after the first commissioning. In addition, HAZOP was done to understand the inherent risk involved in all the cases under study and results gotten would serve as a tool of precautions to operators and stakeholders in period of adversity when facing similar scenario.
{"title":"Understanding the Impacts of Backpressure & Risk Analysis of Different Gas Hydrate Blockage Scenarios on the Integrity of Subsea Flowlines","authors":"E. Umeh, M. Ephraim, Nitonye Samson","doi":"10.2118/207141-ms","DOIUrl":"https://doi.org/10.2118/207141-ms","url":null,"abstract":"\u0000 Offshore oil and gas pipelines are subjected to high pressure and high temperature (HP/HT) from the inner hydrocarbon content during operation. Both the rise in temperature and internal pressure may cause longitudinal expansion of the pipeline. This expansion is restrained or semi-restrained by the pipe end devices and the soil which results in build-up of compression stresses in the pipe wall. These pipelines are also exposed to so many familiar and unfamiliar forces related to static, dynamic and environmental forces.\u0000 This study presents a thorough review of various sources from literature on the integrity challenges of subsea flowlines and pipelines amid challenging operating conditions especially with regards to flow assurance. This paper evaluates the impact of hydrate deposition and agitation on the overall integrity of the subsea flowlines, riser-base and fitting e.g. elbows, valves e.t.c. A bow tie model was developed to determine the threats, causes, consequences, the top event and the impending hydrates that are to be designed and cause blockage and failure. Stress analysis were done with finite element tools which are ANSYS and Autodesk INVENTOR with only the hoop, Von Mises stress and the corresponding back pressure that occurred with the scenario of 0, 10,30,50,70,90 and 100% blockage of flowlines being analyzed and taking the 0% or null blockage as the pilot case with no hydrate formation. The result gotten from both results were validated with hand calculation with excel and the initial design values for the stress values before the initial operation of the wells after the first commissioning. In addition, HAZOP was done to understand the inherent risk involved in all the cases under study and results gotten would serve as a tool of precautions to operators and stakeholders in period of adversity when facing similar scenario.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72868540","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}
Christian Ihwiwhu, Ibi-Ada Itotoi, Udeme John, Nnamdi Obioha, Precious Okoro, Maduabuchi Ndubueze, Edward Bobade, A. Awujoola, Oghenerunor Bekibele, So Adesanya
Understanding the complexity in the distribution of hydrocarbon in a simple structure with flow baffles and connectivity issues is critical in targeting and developing the remaining pay in a mature asset. Subtle facies changes (heterogeneity) can have drastic impact on reservoir fluids movement, and this can be crucial to identifying sweet spots in mature fields. This study evaluated selected reservoirs in Ovhor Field, Niger Delta, Nigeria with the objective of optimising production from the field by targeting undeveloped oil reserves or bypassed pay and gaining an improved understanding of the selected reservoirs to increase the company's reserves limits. The task at the Ovhor field, is complicated by poor stratigraphic seismic resolution over the field. 3-D geological (Sedimentology and stratigraphy) interpretation, Quantitative interpretation results and proper understanding of production data have been used in recognizing flow baffles and undeveloped compartments in the field. The full field 3-D model was constructed in such a way as to capture heterogeneities and the various compartments in the field. This was crucial to aid the simulation of fluid flow in the field for proper history matching, future production, prediction and design of well trajectories to adequately target undeveloped oil in the field. Reservoir property models (Porosity, Permeability and Net-To-Gross) were constructed by biasing log interpreted properties to a defined environment of deposition model whose interpretation captured the heterogeneities expected in the studied reservoirs. At least, two scenarios were modelled for the studied reservoirs to capture the range of uncertainties. This integrated approach led to the identification of bypassed oil in some areas of the selected reservoirs and an improved understanding of the studied reservoirs. Dynamic simulation and production forecast on the 4 reservoirs gave an undeveloped reserve of about 3.82 MMstb from two (2) identified oil restoration activities. These activities included side-tracking and re-perforation of existing wells. New wells have been drilled to test the results of our studies and the results confirmed our findings.
{"title":"Targeting and Developing the Remaining Pay in an Ageing Field: the Ovhor Field Experience","authors":"Christian Ihwiwhu, Ibi-Ada Itotoi, Udeme John, Nnamdi Obioha, Precious Okoro, Maduabuchi Ndubueze, Edward Bobade, A. Awujoola, Oghenerunor Bekibele, So Adesanya","doi":"10.2118/207089-ms","DOIUrl":"https://doi.org/10.2118/207089-ms","url":null,"abstract":"\u0000 Understanding the complexity in the distribution of hydrocarbon in a simple structure with flow baffles and connectivity issues is critical in targeting and developing the remaining pay in a mature asset. Subtle facies changes (heterogeneity) can have drastic impact on reservoir fluids movement, and this can be crucial to identifying sweet spots in mature fields. This study evaluated selected reservoirs in Ovhor Field, Niger Delta, Nigeria with the objective of optimising production from the field by targeting undeveloped oil reserves or bypassed pay and gaining an improved understanding of the selected reservoirs to increase the company's reserves limits.\u0000 The task at the Ovhor field, is complicated by poor stratigraphic seismic resolution over the field. 3-D geological (Sedimentology and stratigraphy) interpretation, Quantitative interpretation results and proper understanding of production data have been used in recognizing flow baffles and undeveloped compartments in the field. The full field 3-D model was constructed in such a way as to capture heterogeneities and the various compartments in the field. This was crucial to aid the simulation of fluid flow in the field for proper history matching, future production, prediction and design of well trajectories to adequately target undeveloped oil in the field.\u0000 Reservoir property models (Porosity, Permeability and Net-To-Gross) were constructed by biasing log interpreted properties to a defined environment of deposition model whose interpretation captured the heterogeneities expected in the studied reservoirs. At least, two scenarios were modelled for the studied reservoirs to capture the range of uncertainties.\u0000 This integrated approach led to the identification of bypassed oil in some areas of the selected reservoirs and an improved understanding of the studied reservoirs. Dynamic simulation and production forecast on the 4 reservoirs gave an undeveloped reserve of about 3.82 MMstb from two (2) identified oil restoration activities. These activities included side-tracking and re-perforation of existing wells. New wells have been drilled to test the results of our studies and the results confirmed our findings.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74578710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the development of predictive optimization models for autonomous rotary drilling systems where emphasis is placed on the shift from human (manual) operation as the driving force for drill rate performance to Quantitative Real-time Prediction (QRP) using machine learning. The methodology employed in this work uses real-time offset drilling data with machine learning models to accurately predict Rate of Penetration (ROP) and determine optimum operating parameters for improved drilling performance. Two optimization models (physics-based and energy conservation) were tested using Artificial Neutral Network (ANN) algorithm. Results of analysis using the model performance assessment criteria; correlation coefficient (R2) and Root Mean Square Error (RMSE), show that drill rate is non-linear in nature and the machine learning model (ANN) using energy conservation is most accurate for predicting ROP due to its ability in establishing a functional feature vector based on learning from past events.
{"title":"Modeling and Predicting Performance of Autonomous Rotary Drilling System Using Machine Learning Techniques","authors":"K. Amadi, I. Iyalla, R. Prabhu","doi":"10.2118/208450-ms","DOIUrl":"https://doi.org/10.2118/208450-ms","url":null,"abstract":"\u0000 This paper presents the development of predictive optimization models for autonomous rotary drilling systems where emphasis is placed on the shift from human (manual) operation as the driving force for drill rate performance to Quantitative Real-time Prediction (QRP) using machine learning. The methodology employed in this work uses real-time offset drilling data with machine learning models to accurately predict Rate of Penetration (ROP) and determine optimum operating parameters for improved drilling performance. Two optimization models (physics-based and energy conservation) were tested using Artificial Neutral Network (ANN) algorithm. Results of analysis using the model performance assessment criteria; correlation coefficient (R2) and Root Mean Square Error (RMSE), show that drill rate is non-linear in nature and the machine learning model (ANN) using energy conservation is most accurate for predicting ROP due to its ability in establishing a functional feature vector based on learning from past events.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77191335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the research work on using a machine learning algorithm to predict the viscosity of Niger Delta oil reservoirs using formation volume factor and fluid density at bubble point pressure as correlating parameters. Oil Viscosity stands out when considering the amount of oil recoverable from the reservoir hence it is an important input into the recovery factor computation, material balance analysis, reservoir simulation/history match, EOR evaluations and many other applications. Laboratory techniques of obtaining oil viscosity are quite expensive and time consuming, hence the need for various mathematical correlations developed for its estimation. Majority of the correlations make use of empirical and experimental relationships developed from analyzing oil samples to obtain a trend to predict viscosity mostly for a basin. None of these has been developed for oil viscosity for Niger Delta fluids. Viscosity has been globally defined as the resistance to shear stresses within the fluid or the resistance of the fluid molecules to deformation. For a typical reservoir fluid system, where the liquid and gas exist in dynamic equilibrium, reservoir fluid composition along with temperature and pressure has been established to determine reservoir fluid viscosity1. Hence for an isothermal system and at a defined pressure in the reservoir the viscosity will be dependent on largely the composition. The reservoir fluid composition is also represented by the reservoir fluid density and the formation volume factor; therefore it is possible to deduce the viscosity of reservoir fluids from the oil density and formation volume factor even though a direct relationship has not been established between these parameters. Therefore, a correlation that can establish a relationship between the specific gravity (density) and FVF with viscosity will have significant value in the oil and industry. The data used for this analysis includes viscosity, formation volume factor, oil density at 2800 sample bubble point pressure. The data was obtained by analyzing over 3500 PVT Analysis reports, extracting the data points using a python work program, cleaning up the data and removing erroneous data, performing preliminary analysis to establish baseline relationships between the data. Supervised learning using a classification tree model was used as the machine learning approach. Seven different machine learning algorithms were reviewed, and the Random Forest Regressor was selected as the most suitable algorithm for the prediction. The model prediction results were quiet encouraging as the model was able to predict viscosity within 10% deviation from the experimental viscosity for over 80% of the cases resulting in about 90% prediction accuracy. The analysis of the results further revealed that the model could better predict viscosity of Medium to Light oil with an R2 value of between 0.90-0.96 without adjusting some obvious erroneous data points. Future of th
{"title":"Developing a Model to Predict Oil Viscosity Using Specific Gravity and Formation Volume Factor as Correlating Parameters","authors":"H. Ijomanta, Olorunfemi Kawonise","doi":"10.2118/208234-ms","DOIUrl":"https://doi.org/10.2118/208234-ms","url":null,"abstract":"\u0000 This paper presents the research work on using a machine learning algorithm to predict the viscosity of Niger Delta oil reservoirs using formation volume factor and fluid density at bubble point pressure as correlating parameters.\u0000 Oil Viscosity stands out when considering the amount of oil recoverable from the reservoir hence it is an important input into the recovery factor computation, material balance analysis, reservoir simulation/history match, EOR evaluations and many other applications.\u0000 Laboratory techniques of obtaining oil viscosity are quite expensive and time consuming, hence the need for various mathematical correlations developed for its estimation. Majority of the correlations make use of empirical and experimental relationships developed from analyzing oil samples to obtain a trend to predict viscosity mostly for a basin. None of these has been developed for oil viscosity for Niger Delta fluids.\u0000 Viscosity has been globally defined as the resistance to shear stresses within the fluid or the resistance of the fluid molecules to deformation.\u0000 For a typical reservoir fluid system, where the liquid and gas exist in dynamic equilibrium, reservoir fluid composition along with temperature and pressure has been established to determine reservoir fluid viscosity1. Hence for an isothermal system and at a defined pressure in the reservoir the viscosity will be dependent on largely the composition. The reservoir fluid composition is also represented by the reservoir fluid density and the formation volume factor; therefore it is possible to deduce the viscosity of reservoir fluids from the oil density and formation volume factor even though a direct relationship has not been established between these parameters. Therefore, a correlation that can establish a relationship between the specific gravity (density) and FVF with viscosity will have significant value in the oil and industry.\u0000 The data used for this analysis includes viscosity, formation volume factor, oil density at 2800 sample bubble point pressure. The data was obtained by analyzing over 3500 PVT Analysis reports, extracting the data points using a python work program, cleaning up the data and removing erroneous data, performing preliminary analysis to establish baseline relationships between the data.\u0000 Supervised learning using a classification tree model was used as the machine learning approach. Seven different machine learning algorithms were reviewed, and the Random Forest Regressor was selected as the most suitable algorithm for the prediction.\u0000 The model prediction results were quiet encouraging as the model was able to predict viscosity within 10% deviation from the experimental viscosity for over 80% of the cases resulting in about 90% prediction accuracy. The analysis of the results further revealed that the model could better predict viscosity of Medium to Light oil with an R2 value of between 0.90-0.96 without adjusting some obvious erroneous data points.\u0000 Future of th","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79262998","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}
Crude oil, a base for more than 6000 products that we use on a daily basis, accounts for 33% of global energy consumption. However, the outbreak and transmission of COVID-19 had significant implications for the entire value chain in the oil industry. The price crash and the fluctuations in price is known to have far reaching effect on global economies, with Nigeria hard. It has therefore become imperative to develop a tool for forecasting the price of crude oil in order to minimise the risks associated with volatility in oil prices and also be able to do proper planning. Hence, this article proposed a hybrid forecasting model involving a classical and machine learning techniques – autoregressive neural network, in determining the prices of crude oil. The monthly data used were obtained from the Central Bank of Nigeria website, spanning January 2006 to October 2020. Statistical efficiency was computed for the hybrid, and the models from which the proposed hybrid was built, using the percent relative efficiency. Analyses showed that the efficiency of the hybrid model, at 20 and 100 hidden neurons, was higher than that of the individual models, the latter being the best performing. The study recommends urgent diversification of the economy in order not for the nation to be plunged into a seemingly unending recession.
{"title":"Efficient Crude Oil Pricing Using a Machine Learning Approach","authors":"O. Falode, C. Udomboso","doi":"10.2118/207152-ms","DOIUrl":"https://doi.org/10.2118/207152-ms","url":null,"abstract":"\u0000 Crude oil, a base for more than 6000 products that we use on a daily basis, accounts for 33% of global energy consumption. However, the outbreak and transmission of COVID-19 had significant implications for the entire value chain in the oil industry. The price crash and the fluctuations in price is known to have far reaching effect on global economies, with Nigeria hard. It has therefore become imperative to develop a tool for forecasting the price of crude oil in order to minimise the risks associated with volatility in oil prices and also be able to do proper planning. Hence, this article proposed a hybrid forecasting model involving a classical and machine learning techniques – autoregressive neural network, in determining the prices of crude oil. The monthly data used were obtained from the Central Bank of Nigeria website, spanning January 2006 to October 2020. Statistical efficiency was computed for the hybrid, and the models from which the proposed hybrid was built, using the percent relative efficiency. Analyses showed that the efficiency of the hybrid model, at 20 and 100 hidden neurons, was higher than that of the individual models, the latter being the best performing. The study recommends urgent diversification of the economy in order not for the nation to be plunged into a seemingly unending recession.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85833579","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. Sanusi, Adenike Omisore, Eyituoyo Blankson, Chinedu Anyanwu, Obehi Eremiokhale
With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on "Comms-on-Power" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought. In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.
{"title":"Estimation of Bottom Hole Pressure in Electrical Submersible Pump Wells using Machine Learning Technique","authors":"S. Sanusi, Adenike Omisore, Eyituoyo Blankson, Chinedu Anyanwu, Obehi Eremiokhale","doi":"10.2118/207122-ms","DOIUrl":"https://doi.org/10.2118/207122-ms","url":null,"abstract":"\u0000 With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on \"Comms-on-Power\" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought.\u0000 In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85873615","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}
Renewable energy in our world today has greatly helped the ecosystem by reducing the amount of carbon content in the atmosphere. Recent studies have shown that the dependence on the National grid and fossil fuels for generating power for pumps is becoming alarming and as such, an alternative source for energy generation to power the pump system necessitated this research. The research relies on solar-generated power for driving pumps as opposed to fossil fuels. A submersible centrifugal pump was used because of its wide usage in various industries such as Oil and Energy, Pharmaceutical, Breweries, Production industries, Water corporations, Domestic and Commercial buildings, etc. We designed and constructed an automatic solar-powered pump system, integrated, and programmed the sensors using Arduino microcontroller and C++ programming language, respectively. We analyzed the telemetry data from the sensors and predicted the illuminance of light on the solar panel and sent the information via a web server using a GSM module. The solar-based pumping system consists of a submersible centrifugal pump, solar panel, solar charge controller, battery, remote controller, GSM module, photo sensor and a liquid level sensor. The photo sensor returns values ranging from 0 to 1023. The higher values: 700 – 1023 indicate that the sensor is in darker surroundings. The lower values: 0 - 650 indicate lighter surroundings when there is sufficient light on the sensor or its surroundings on the web server which display the plotted values in real-time. The system has been found to be viable and economical in the long run compared to the conventional system which uses fossil fuels. The solar energy received from the sun is converted to electrical energy by the solar panel. A proportion of the energy is used during the day while some is stored in the battery to be used at night or when the weather is cloudy. The controller regulates the liquid level in storage with the aid of liquid level sensor and affords the user the opportunity to control the system remotely. This system can be used for small and remote applications.
{"title":"Design and Development of a Solar-Powered Pump System with Liquid Level Sensor and Controller Using Internet of Things Iot Technology","authors":"Chinonyelum Ejimuda, Kingsley Okoli","doi":"10.2118/207188-ms","DOIUrl":"https://doi.org/10.2118/207188-ms","url":null,"abstract":"\u0000 Renewable energy in our world today has greatly helped the ecosystem by reducing the amount of carbon content in the atmosphere. Recent studies have shown that the dependence on the National grid and fossil fuels for generating power for pumps is becoming alarming and as such, an alternative source for energy generation to power the pump system necessitated this research. The research relies on solar-generated power for driving pumps as opposed to fossil fuels. A submersible centrifugal pump was used because of its wide usage in various industries such as Oil and Energy, Pharmaceutical, Breweries, Production industries, Water corporations, Domestic and Commercial buildings, etc.\u0000 We designed and constructed an automatic solar-powered pump system, integrated, and programmed the sensors using Arduino microcontroller and C++ programming language, respectively. We analyzed the telemetry data from the sensors and predicted the illuminance of light on the solar panel and sent the information via a web server using a GSM module. The solar-based pumping system consists of a submersible centrifugal pump, solar panel, solar charge controller, battery, remote controller, GSM module, photo sensor and a liquid level sensor. The photo sensor returns values ranging from 0 to 1023. The higher values: 700 – 1023 indicate that the sensor is in darker surroundings. The lower values: 0 - 650 indicate lighter surroundings when there is sufficient light on the sensor or its surroundings on the web server which display the plotted values in real-time. The system has been found to be viable and economical in the long run compared to the conventional system which uses fossil fuels. The solar energy received from the sun is converted to electrical energy by the solar panel. A proportion of the energy is used during the day while some is stored in the battery to be used at night or when the weather is cloudy. The controller regulates the liquid level in storage with the aid of liquid level sensor and affords the user the opportunity to control the system remotely. This system can be used for small and remote applications.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90556575","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}
Application of artificial intelligence (AI) and machine learning (ML) is becoming a new addition to the traditional reservoir characterization, petrophysics and monitoring practice in oil and gas industry. Accurate reservoir characterization is the driver to optimize production performance throughout the life of a field. Developing physics-based data models are the key for applying ML techniques to solve complex reservoir problems. The main objective of this study is to apply machine learning techniques in reservoir Characterization. This was achieved via machine learning algorithm using permeability and porosity as the investigative variables. Permeability and porosity of a reservoir were predicted using machine learning technique with the aid of XLSTAT in Excel. The general performance and predictability of the technique as applied to permeability and porosity predictions were compared. From the results obtained, it was observed that the machine learning model used was able to predict about 98% of the permeability and 81% of the porosity. The results from Al and ML will reinforce reservoir engineers to carry out effective reservoir characterization with powerful algorithms based on machine learning techniques. Hence, it can therefore be inferred that machine learning approach has the ability to predict reservoir parameters.
{"title":"Application of Machine Learning Techniques in Reservoir Characterization","authors":"Edet Ita Okon, D. Appah","doi":"10.2118/208248-ms","DOIUrl":"https://doi.org/10.2118/208248-ms","url":null,"abstract":"\u0000 Application of artificial intelligence (AI) and machine learning (ML) is becoming a new addition to the traditional reservoir characterization, petrophysics and monitoring practice in oil and gas industry. Accurate reservoir characterization is the driver to optimize production performance throughout the life of a field. Developing physics-based data models are the key for applying ML techniques to solve complex reservoir problems. The main objective of this study is to apply machine learning techniques in reservoir Characterization. This was achieved via machine learning algorithm using permeability and porosity as the investigative variables. Permeability and porosity of a reservoir were predicted using machine learning technique with the aid of XLSTAT in Excel. The general performance and predictability of the technique as applied to permeability and porosity predictions were compared. From the results obtained, it was observed that the machine learning model used was able to predict about 98% of the permeability and 81% of the porosity. The results from Al and ML will reinforce reservoir engineers to carry out effective reservoir characterization with powerful algorithms based on machine learning techniques. Hence, it can therefore be inferred that machine learning approach has the ability to predict reservoir parameters.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88608890","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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