Justice Osuala, D. I. Egu, A. J. Ilozobhie, Blessing Ogechi Nwojiji
Studies show that an average of 35% of reservoir acid stimulation operations executed every year fails because of limited knowledge of downhole acid placement. Existing models designed for acid pre-flush volumes are limited to Linear, Radial and Ellipsoidal reservoir geometries, therefore, do not account for geological drifts of a typical heterogenic reservoir. This can be erroneous while estimating acid placement volumes as reservoirs can deviate from defined flow geometries due to their dynamic and heterogeneous nature, thereby challenging to estimate acid volumes precisely for stimulations. This study aims to foster sustainability in reservoir flow engineering by deriving a mathematical model that evaluates volumes for reservoirs with flow geometries that deviate from linear and radial. This was established to help introduce a new geometry contributing to the accountability of complex and heterogeneous reservoirs. Sensitivity analysis and investigation using reservoir core data from SPDC Petroleum Chemistry Laboratory were carried out to understand the relationship between Linear, Radial and Modified flow geometries. Analytical results for linear, radial and the fied were generated. These results proved the precision of the modified equation for calculating pre-flush acid volume for reservoir acid stimulation operation.
{"title":"Enhancing Reservoir Stimulation through Mathematical Remodeling of Pre-Flush Acidizing Volume Algorithm for Different Reservoir Flow Geometries","authors":"Justice Osuala, D. I. Egu, A. J. Ilozobhie, Blessing Ogechi Nwojiji","doi":"10.2118/211916-ms","DOIUrl":"https://doi.org/10.2118/211916-ms","url":null,"abstract":"\u0000 Studies show that an average of 35% of reservoir acid stimulation operations executed every year fails because of limited knowledge of downhole acid placement. Existing models designed for acid pre-flush volumes are limited to Linear, Radial and Ellipsoidal reservoir geometries, therefore, do not account for geological drifts of a typical heterogenic reservoir. This can be erroneous while estimating acid placement volumes as reservoirs can deviate from defined flow geometries due to their dynamic and heterogeneous nature, thereby challenging to estimate acid volumes precisely for stimulations. This study aims to foster sustainability in reservoir flow engineering by deriving a mathematical model that evaluates volumes for reservoirs with flow geometries that deviate from linear and radial. This was established to help introduce a new geometry contributing to the accountability of complex and heterogeneous reservoirs. Sensitivity analysis and investigation using reservoir core data from SPDC Petroleum Chemistry Laboratory were carried out to understand the relationship between Linear, Radial and Modified flow geometries. Analytical results for linear, radial and the fied were generated. These results proved the precision of the modified equation for calculating pre-flush acid volume for reservoir acid stimulation operation.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"103 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":"123722279","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. Adeniyi, A. Igbafe, Olokpa Ebis, A. Ogunyemi, Sikiru Yusuff, O. J. Oyebode
Drilling in search for hydrocarbon is an essential component of exploration and production activities. Chemicals, Drill rig, Casing, Tubing, Drill pipes and bits are basic requirements to successfully drill a well. Rotary Drilling rig is very crucial among the basic requirements. A major function of rotary drilling rig, is continuous circulation of drilling fluid and removal of cuttings. Hence, this paper focused on the design and construction of drilling rig prototype, for training purposes in academic environment. Components were constructed from the most suitable materials obtained from metal scraps individually, and when put together forms an integrated system that enables the drilling process to make a well. The prototype was produced successfully. The mixing hopper, hoisting and the mud circulatory systems were fully incorporated and connected. The rig prototype was, in principle, to transport fluid from the mud pit up the stand pipe to the swivel via the rotary hose down the drill pipe to the annulus and back to the mud pit through the shale shaker, De-sander, De-gasser, De-silter units, via the mud return line. The drawworks is to lift the drill pipe and lower it back into the rotary table with the aid of the drawworks motor and a top drive system.
{"title":"Design and Construction of Rotary Drilling Rig Prototype","authors":"A. Adeniyi, A. Igbafe, Olokpa Ebis, A. Ogunyemi, Sikiru Yusuff, O. J. Oyebode","doi":"10.2118/211999-ms","DOIUrl":"https://doi.org/10.2118/211999-ms","url":null,"abstract":"\u0000 Drilling in search for hydrocarbon is an essential component of exploration and production activities. Chemicals, Drill rig, Casing, Tubing, Drill pipes and bits are basic requirements to successfully drill a well. Rotary Drilling rig is very crucial among the basic requirements. A major function of rotary drilling rig, is continuous circulation of drilling fluid and removal of cuttings. Hence, this paper focused on the design and construction of drilling rig prototype, for training purposes in academic environment. Components were constructed from the most suitable materials obtained from metal scraps individually, and when put together forms an integrated system that enables the drilling process to make a well. The prototype was produced successfully. The mixing hopper, hoisting and the mud circulatory systems were fully incorporated and connected. The rig prototype was, in principle, to transport fluid from the mud pit up the stand pipe to the swivel via the rotary hose down the drill pipe to the annulus and back to the mud pit through the shale shaker, De-sander, De-gasser, De-silter units, via the mud return line. The drawworks is to lift the drill pipe and lower it back into the rotary table with the aid of the drawworks motor and a top drive system.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"169 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":"126782234","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}
Production optimization is often required to manage increase of undesired reservoir fluids especially water in oil and gas wells. However, this activity needs to be guided by science and data rather than a trial-and-error approach of changing the operating conditions of the well to determine the corresponding production response. Well performance models are often used to predict well behavior at different operating conditions but one of the disadvantages of this method is the inability to predict the water cut based on given well parameters. In this work, we applied the random Forest Regression model, well test data and well performance model to predict the expected water cut while changing the operating conditions of a well. We had used four wells to demonstrate the application of machine learning to produced water prediction under different operating conditions. Well performance model which is a combination of Presssure Volume Temperature (PVT) model, inflow performance relationship (IPR) model and vertical lift performance (VLP) model was used to generate the well parameters transferred to the machine learning algorithm. A histogram and box plot were first drawn to understand the distribution of the data and filter the outliers within the dataset because outliers skew the model results. A correlation matrix was now used to understand the relationship between the water cut and the following variables: Flowing Tubing Head Pressure, the Bean Size, the Gas Oil Ratio, and liquid production. Thereafter the Random Forest model was applied to the well parameters to get the predicted values. After getting our predicted values from our model, the model results were evaluated with three regression evaluation metrics, the mean absolute error, the mean squared error and the root mean squared error to compare the predicted water cut values with the actual values and return the margin of error in the predictions. The Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error results were within acceptable tolerance. Therefore, given the minimal error values we can conclude that the model can successfully predict water cut values at different operating conditions. Based on our evaluation, the bar chart predicted values and actual values showed minimal error margins indicating the model's accuracy can be trusted. This paper presents a novel way to estimate the water cut of a well under various operating conditions, a prediction that is not available using existing well performance models.
{"title":"Application of Machine Learning Algorithm for Predicting Produced Water Under Various Operating Conditions in an Oilwell","authors":"Eriagbaraoluwa Adesina, B. Olusola","doi":"10.2118/211921-ms","DOIUrl":"https://doi.org/10.2118/211921-ms","url":null,"abstract":"\u0000 Production optimization is often required to manage increase of undesired reservoir fluids especially water in oil and gas wells. However, this activity needs to be guided by science and data rather than a trial-and-error approach of changing the operating conditions of the well to determine the corresponding production response. Well performance models are often used to predict well behavior at different operating conditions but one of the disadvantages of this method is the inability to predict the water cut based on given well parameters. In this work, we applied the random Forest Regression model, well test data and well performance model to predict the expected water cut while changing the operating conditions of a well.\u0000 We had used four wells to demonstrate the application of machine learning to produced water prediction under different operating conditions. Well performance model which is a combination of Presssure Volume Temperature (PVT) model, inflow performance relationship (IPR) model and vertical lift performance (VLP) model was used to generate the well parameters transferred to the machine learning algorithm. A histogram and box plot were first drawn to understand the distribution of the data and filter the outliers within the dataset because outliers skew the model results. A correlation matrix was now used to understand the relationship between the water cut and the following variables: Flowing Tubing Head Pressure, the Bean Size, the Gas Oil Ratio, and liquid production.\u0000 Thereafter the Random Forest model was applied to the well parameters to get the predicted values. After getting our predicted values from our model, the model results were evaluated with three regression evaluation metrics, the mean absolute error, the mean squared error and the root mean squared error to compare the predicted water cut values with the actual values and return the margin of error in the predictions. The Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error results were within acceptable tolerance. Therefore, given the minimal error values we can conclude that the model can successfully predict water cut values at different operating conditions.\u0000 Based on our evaluation, the bar chart predicted values and actual values showed minimal error margins indicating the model's accuracy can be trusted.\u0000 This paper presents a novel way to estimate the water cut of a well under various operating conditions, a prediction that is not available using existing well performance models.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"16 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113955893","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}
Reservoir fluid PVT properties are measured in the laboratory for various use in reservoir engineering evaluation and estimation. Despite the indispensability of these PVT parameters, PVT lab data are seldomly available and if available may be unreliable. Instead, various empirical models have been developed and used in the industry. These empirical models are inherently inaccurate when used to predict PVT properties of fluid from different geological region with different depositional environment and fingerprint. Artificial Intelligence (AI) has evolved over the years and provided some algorithms with potentials to develop accurate predictive model for the prediction of bubblepoint pressure. This work tested some AI algorithms, compared performances and choose random forest regression algorithm in developing a robust predictive model for the estimation of bubblepoint pressure. Two thousand five hundred and twenty-two datasets obtained from oil reservoirs in different geographical locations were used for the feature scaling of input data, training and testing of the models. The independent variables, gas-oil ratio, temperature, oil density and gas density were confirmed to have key influence on the dependent variable Bubblepoint pressure The random forest model developed uses ensemble learning approach, combines predictions from multiple machine learning algorithms by averaging all predictions to make a more accurate prediction. The ‘forest’ generated by the random forest algorithm was trained through bootstrap aggregating. This is an ensemble meta-algorithm that improves the accuracy of machine learning algorithms. Percentage data split was 70% training and 30% testing. The reliability, accuracy and completeness of the predictive model capability were computed through performance indices such as the root mean square error (RMSE) and mean absolute error (MAE). The best network architecture was determined along with the corresponding test set RMSE, and Correlation coefficient. Statistical and graphical error analysis of the results showed that the random forest model performed better than existing models with 0.98 correlation coefficients for bubblepoint pressure. Better accuracy of reservoir properties prediction could be achieved using this random forest reservoir fluid properties prediction model.
{"title":"Random Forest Ensemble Model for Reservoir Fluid Property Prediction","authors":"Y. Adeeyo","doi":"10.2118/212044-ms","DOIUrl":"https://doi.org/10.2118/212044-ms","url":null,"abstract":"\u0000 Reservoir fluid PVT properties are measured in the laboratory for various use in reservoir engineering evaluation and estimation. Despite the indispensability of these PVT parameters, PVT lab data are seldomly available and if available may be unreliable. Instead, various empirical models have been developed and used in the industry. These empirical models are inherently inaccurate when used to predict PVT properties of fluid from different geological region with different depositional environment and fingerprint. Artificial Intelligence (AI) has evolved over the years and provided some algorithms with potentials to develop accurate predictive model for the prediction of bubblepoint pressure.\u0000 This work tested some AI algorithms, compared performances and choose random forest regression algorithm in developing a robust predictive model for the estimation of bubblepoint pressure.\u0000 Two thousand five hundred and twenty-two datasets obtained from oil reservoirs in different geographical locations were used for the feature scaling of input data, training and testing of the models. The independent variables, gas-oil ratio, temperature, oil density and gas density were confirmed to have key influence on the dependent variable Bubblepoint pressure\u0000 The random forest model developed uses ensemble learning approach, combines predictions from multiple machine learning algorithms by averaging all predictions to make a more accurate prediction. The ‘forest’ generated by the random forest algorithm was trained through bootstrap aggregating. This is an ensemble meta-algorithm that improves the accuracy of machine learning algorithms. Percentage data split was 70% training and 30% testing.\u0000 The reliability, accuracy and completeness of the predictive model capability were computed through performance indices such as the root mean square error (RMSE) and mean absolute error (MAE). The best network architecture was determined along with the corresponding test set RMSE, and Correlation coefficient.\u0000 Statistical and graphical error analysis of the results showed that the random forest model performed better than existing models with 0.98 correlation coefficients for bubblepoint pressure. Better accuracy of reservoir properties prediction could be achieved using this random forest reservoir fluid properties prediction model.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"29 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":"127615343","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 results of pressure transient analysis (PTA) are very important in reservoir characterization; however, this analysis can be affected by some non-reservoir behavior such as gas breakthrough, phase segregation in the wellbore, tidal effects, all of which can perturb the result accuracy. When data is acquired for PTA offshore, it can contain tidal effect, causing noise which can lead to misinterpretation when the test is analyzed, hence its impact should be accounted for in the analysis. Tides are experienced as the rise and fall of sea levels due to the variation in the earth's gravitational potential exerted by the moon and the sun, and the rotation of the Earth. Tidal signals have been observed to mask late time response for pressure build up tests and will significantly hinder correct interpretation of reservoir boundaries if left unaddressed. The effects of tidal pressure signals on the pressure derivative of pressure build-up tests are studied with the aim of comprehensively exploring the deviation from expected responses given known reservoir boundary conditions. Subsequently a refined method for pure tidal component removal from pressure derivative data is presented and compared to simpler Bourdet smoothening (L) and filtration of data points used in evaluation. This work focused on an efficient method to analyze data containing tidal effects. The Bourdet derivative and log cycle filtration was effective in removing tidal signal effects on late time boundary identification with the drawback being having multiple possible interpretations of the IARF. Extracting the tidal signal gave a more defined IARF period and late time boundary effect period with only minor oscillations in the late time but the rigor of extracting the tidal signal without sufficient regional tidal information may prove to major hindrance to this process.
{"title":"A Comparison of Tidal Signal Extraction and Bourdet Smoothening for Removal of Tidal Effect Induced Artifacts in Pressure Transient Analysis","authors":"David Nnamdi, K. Ochie, R. Moghanloo","doi":"10.2118/212009-ms","DOIUrl":"https://doi.org/10.2118/212009-ms","url":null,"abstract":"\u0000 The results of pressure transient analysis (PTA) are very important in reservoir characterization; however, this analysis can be affected by some non-reservoir behavior such as gas breakthrough, phase segregation in the wellbore, tidal effects, all of which can perturb the result accuracy. When data is acquired for PTA offshore, it can contain tidal effect, causing noise which can lead to misinterpretation when the test is analyzed, hence its impact should be accounted for in the analysis. Tides are experienced as the rise and fall of sea levels due to the variation in the earth's gravitational potential exerted by the moon and the sun, and the rotation of the Earth. Tidal signals have been observed to mask late time response for pressure build up tests and will significantly hinder correct interpretation of reservoir boundaries if left unaddressed. The effects of tidal pressure signals on the pressure derivative of pressure build-up tests are studied with the aim of comprehensively exploring the deviation from expected responses given known reservoir boundary conditions. Subsequently a refined method for pure tidal component removal from pressure derivative data is presented and compared to simpler Bourdet smoothening (L) and filtration of data points used in evaluation.\u0000 This work focused on an efficient method to analyze data containing tidal effects. The Bourdet derivative and log cycle filtration was effective in removing tidal signal effects on late time boundary identification with the drawback being having multiple possible interpretations of the IARF. Extracting the tidal signal gave a more defined IARF period and late time boundary effect period with only minor oscillations in the late time but the rigor of extracting the tidal signal without sufficient regional tidal information may prove to major hindrance to this process.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"32 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":"134317957","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. Momodu, F. M. Kelechi, Augustine Soro, S. Shittu, Kelechi Victoria Osime, Emmanuel Oduyemi Olawunmi
The expansion of gas utilization systems, together with Nigeria's present climate objective, makes CCS a must-do for the country. The Niger Delta Basin has been identified as an excellent setting for carbon capture and storage (CCS), particularly in depleted reservoirs, according to a basin-wide evaluation. However, not all carbon-depleted reservoirs are appropriate for carbon storage. The suitability of the western Niger Delta basin for CCS is assessed in this research. This study looked at five reservoirs in the western section of the basin. The storage capability of the region's reservoirs was assessed using Screening Criteria for Carbon Storage, as well as well logs, seismic, reservoir properties and petrophysical data. These reservoirs are proven to fit several characteristics, including seismicity, size, faulting intensity, reservoir depth, maturity, hydrocarbon potentials, climate, and hydrogeology. The findings of this study may be used as a benchmark for identifying prospective storage locations within the basin and extended to other sedimentary basins.
{"title":"Prospective Application of Carbon Capture and Storage: A Case Study of Field X in OML Y in the Niger Delta Basin","authors":"E. Momodu, F. M. Kelechi, Augustine Soro, S. Shittu, Kelechi Victoria Osime, Emmanuel Oduyemi Olawunmi","doi":"10.2118/212005-ms","DOIUrl":"https://doi.org/10.2118/212005-ms","url":null,"abstract":"\u0000 The expansion of gas utilization systems, together with Nigeria's present climate objective, makes CCS a must-do for the country. The Niger Delta Basin has been identified as an excellent setting for carbon capture and storage (CCS), particularly in depleted reservoirs, according to a basin-wide evaluation. However, not all carbon-depleted reservoirs are appropriate for carbon storage. The suitability of the western Niger Delta basin for CCS is assessed in this research. This study looked at five reservoirs in the western section of the basin. The storage capability of the region's reservoirs was assessed using Screening Criteria for Carbon Storage, as well as well logs, seismic, reservoir properties and petrophysical data. These reservoirs are proven to fit several characteristics, including seismicity, size, faulting intensity, reservoir depth, maturity, hydrocarbon potentials, climate, and hydrogeology. The findings of this study may be used as a benchmark for identifying prospective storage locations within the basin and extended to other sedimentary basins.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"179 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":"133889607","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, immediate action has been required to prevent climate change effects through clean energy. However, this step represents a threat of existence to third-world countries such as Nigeria, which relies heavily on royalties and tax revenues from oil and gas reserves. The Nigerian government is a signatory to the Paris Agreement, but as part of that decarbonization project and the transition to net-zero, issues of gas come up, and we talk of just and equitable transition. It is thus important to consider the various realities of developing economies. This paper discussed Nigeria's role in a fair and balanced global energy transition towards achieving net-zero by 2050, without jeopardizing the lives of millions. In this study, the prospects, and challenges of using natural gas as a driver of sustainability and energy transition to leverage the massive gas potential across the country is also presented to build an economy that can support a sustainable energy future.
{"title":"Assessment of Nigeria's Role in the Global Energy Transition d Maintaining Economic Stability","authors":"I. Koffi, Israel Bassey","doi":"10.2118/211959-ms","DOIUrl":"https://doi.org/10.2118/211959-ms","url":null,"abstract":"\u0000 Over the years, immediate action has been required to prevent climate change effects through clean energy. However, this step represents a threat of existence to third-world countries such as Nigeria, which relies heavily on royalties and tax revenues from oil and gas reserves. The Nigerian government is a signatory to the Paris Agreement, but as part of that decarbonization project and the transition to net-zero, issues of gas come up, and we talk of just and equitable transition. It is thus important to consider the various realities of developing economies.\u0000 This paper discussed Nigeria's role in a fair and balanced global energy transition towards achieving net-zero by 2050, without jeopardizing the lives of millions. In this study, the prospects, and challenges of using natural gas as a driver of sustainability and energy transition to leverage the massive gas potential across the country is also presented to build an economy that can support a sustainable energy future.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"32 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114092074","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, J. Ugherughe
Nigeria is richly blessed with crude oil, with a proven reserve of 37billion barrels. Despite the abundance of this "black gold", Nigeria has over the years lacked the capacity to meet the country's demand for petroleum products locally and has resorted to the importation of petroleum products. This is largely due to the fact that the four state-owned conventional refineries, with a combined refining capacity of 445,000 bpd have been operating below optimal conditions, with a combined capacity utilization of 17% in 10years, from 2009 to 2018. Though establishing conventional refineries is highly capital intensive and significantly takes a long time to build and commission, the modular refinery option is however a less capital intensive alternative. This paper discusses the vital roles or importance of modular refineries as well as how it impacts on the availability of petroleum products in the Nigeria. It was discovered that Nigeria has lots of benefits to reap from exploiting modular refinery initiative, amongst which are eliminating fuel shortages and deficits, job creation, overall improvement of the economy and GDP growth, conservation of foreign exchange, among others. It was concluded that the right policy drive to encourage investors to dive into this initiative be put in place to enable Nigeria transit into an exporter of petroleum products.
{"title":"Imperatives of Modular Refineries and their Impact on Product Availability in Nigeria","authors":"E. Ekeinde, A. Dosunmu, D. C. Okujagu, J. Ugherughe","doi":"10.2118/211932-ms","DOIUrl":"https://doi.org/10.2118/211932-ms","url":null,"abstract":"\u0000 Nigeria is richly blessed with crude oil, with a proven reserve of 37billion barrels. Despite the abundance of this \"black gold\", Nigeria has over the years lacked the capacity to meet the country's demand for petroleum products locally and has resorted to the importation of petroleum products. This is largely due to the fact that the four state-owned conventional refineries, with a combined refining capacity of 445,000 bpd have been operating below optimal conditions, with a combined capacity utilization of 17% in 10years, from 2009 to 2018. Though establishing conventional refineries is highly capital intensive and significantly takes a long time to build and commission, the modular refinery option is however a less capital intensive alternative. This paper discusses the vital roles or importance of modular refineries as well as how it impacts on the availability of petroleum products in the Nigeria. It was discovered that Nigeria has lots of benefits to reap from exploiting modular refinery initiative, amongst which are eliminating fuel shortages and deficits, job creation, overall improvement of the economy and GDP growth, conservation of foreign exchange, among others. It was concluded that the right policy drive to encourage investors to dive into this initiative be put in place to enable Nigeria transit into an exporter of petroleum products.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"176 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":"123271031","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}
Several analytical techniques have been identified to obtain reliable estimates of production. Out of these numerous methods, decline curves are the most extensively used technique for the production forecast of Niger Delta Reservoirs. However, a major setback in applying the decline curve is its inability to adapt predictions to different past operational scenarios and uncertainties. With the emergence of big data and increasing computational power, machine learning techniques are increasingly being used to solve problems like this in the oil and gas industry. The objective of this paper is to present the application of a machine learning-based framework to predict the future performance of producing wells in some reservoirs in Niger Delta. In this paper, a machine learning model (Neural Networks model) was used to detect the non-linear relationship between the inputs in the production data and predict the future production rate of wells. The model is trained using available data from a Niger Delta Reservoir. Further data, excluded from the training data set, was used to assess the ability of the neural network to rapidly learn the basic shape of the time series data and model the non-linear relationship of the data for prediction. The different case studies are compared to forecasts from conventional decline curves to demonstrate the advantage of applying machine learning techniques to production forecasting. The proposed technique indicates high accurate prediction and learning performance for crude oil forecast of producing wells, especially for cases with changing operating conditions. The study also reflects that the performance of the model is largely influenced by the model-optimization technique. The research work provides empirical evidence that the proposed model can be applied to production forecasting, addressing complexities that other statistical forecast methods cannot implement. The proposed application of computational techniques in forecasting problems has proven to be a robust and reliable method of forecasting the future performance of producing wells. The procedures adopted in this work can also be extended to wells outside of the Niger Delta.
{"title":"Machine Learning Prediction Versus Decline Curve Prediction: A Niger Delta Case Study","authors":"Ifeoluwa Jayeola, B. Olusola, K. Orodu","doi":"10.2118/211956-ms","DOIUrl":"https://doi.org/10.2118/211956-ms","url":null,"abstract":"\u0000 Several analytical techniques have been identified to obtain reliable estimates of production. Out of these numerous methods, decline curves are the most extensively used technique for the production forecast of Niger Delta Reservoirs. However, a major setback in applying the decline curve is its inability to adapt predictions to different past operational scenarios and uncertainties. With the emergence of big data and increasing computational power, machine learning techniques are increasingly being used to solve problems like this in the oil and gas industry. The objective of this paper is to present the application of a machine learning-based framework to predict the future performance of producing wells in some reservoirs in Niger Delta. In this paper, a machine learning model (Neural Networks model) was used to detect the non-linear relationship between the inputs in the production data and predict the future production rate of wells. The model is trained using available data from a Niger Delta Reservoir. Further data, excluded from the training data set, was used to assess the ability of the neural network to rapidly learn the basic shape of the time series data and model the non-linear relationship of the data for prediction. The different case studies are compared to forecasts from conventional decline curves to demonstrate the advantage of applying machine learning techniques to production forecasting. The proposed technique indicates high accurate prediction and learning performance for crude oil forecast of producing wells, especially for cases with changing operating conditions. The study also reflects that the performance of the model is largely influenced by the model-optimization technique. The research work provides empirical evidence that the proposed model can be applied to production forecasting, addressing complexities that other statistical forecast methods cannot implement. The proposed application of computational techniques in forecasting problems has proven to be a robust and reliable method of forecasting the future performance of producing wells. The procedures adopted in this work can also be extended to wells outside of the Niger Delta.","PeriodicalId":399294,"journal":{"name":"Day 2 Tue, August 02, 2022","volume":"1 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":"122910368","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}
Gas lift technology involves the introduction of gas in the tubing to improve vertical lift performance and over all well productivity. However, when wax is deposited in the tubing, the pressure drop across tubing is increased and vertical lift performance is adversely impacted. This paper reviews the performance of two wells known to have wax deposition issues leading to sub-optimal production, thus necessitating intermittent paraffin inhibition /hot oiling which have associated costs. A Fluid Thermodynamics model which demonstrated that production from the two wells can be optimized by gas lifting wells at points deeper in the tubing than the nucleating points at a threshold gas lift temperature was developed. The minimum gas lift temperature at any given pressure required to attain this flow assurance solution was simulated from the model developed. The model illustrates that a thermodynamic state can be attained without the use of an inline heater. This was due to the high discharge of thermal energy from the lift gas supplied from the gas lift manifold. Results from model application to the two case study wells showed improvement of flow rates from sub-optimal values to steady rates of total increments of about 1,000 Barrels of Oil Per Day. In addition, wax deposition ceased as confirmed from the laboratory re-estimation of the Wax Appearance Temperature (WAT) of the wellbore fluids. This model application eliminated yearly remediation operations such as hot oiling operations that was in place to manage and ensure that the wells produced continually, resulting in an annual cost saving of about $30,000 per well. This Thermal inhibition method can be applied in all wax producers to eliminate or reduce wax in tubing and hence the flow line.
{"title":"Paraffin Inhibition in the Tubing of a Gas-Lifted Production Well Using Pre-Conditioned High Temperature Gas","authors":"K. Nwankwo","doi":"10.2118/212034-ms","DOIUrl":"https://doi.org/10.2118/212034-ms","url":null,"abstract":"\u0000 Gas lift technology involves the introduction of gas in the tubing to improve vertical lift performance and over all well productivity. However, when wax is deposited in the tubing, the pressure drop across tubing is increased and vertical lift performance is adversely impacted. This paper reviews the performance of two wells known to have wax deposition issues leading to sub-optimal production, thus necessitating intermittent paraffin inhibition /hot oiling which have associated costs.\u0000 A Fluid Thermodynamics model which demonstrated that production from the two wells can be optimized by gas lifting wells at points deeper in the tubing than the nucleating points at a threshold gas lift temperature was developed. The minimum gas lift temperature at any given pressure required to attain this flow assurance solution was simulated from the model developed. The model illustrates that a thermodynamic state can be attained without the use of an inline heater. This was due to the high discharge of thermal energy from the lift gas supplied from the gas lift manifold.\u0000 Results from model application to the two case study wells showed improvement of flow rates from sub-optimal values to steady rates of total increments of about 1,000 Barrels of Oil Per Day. In addition, wax deposition ceased as confirmed from the laboratory re-estimation of the Wax Appearance Temperature (WAT) of the wellbore fluids. This model application eliminated yearly remediation operations such as hot oiling operations that was in place to manage and ensure that the wells produced continually, resulting in an annual cost saving of about $30,000 per well. This Thermal inhibition method can be applied in all wax producers to eliminate or reduce wax in tubing and hence the flow line.","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":"127687228","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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