Mohammed Bashir Abdullahi, A. Sulaiman, U. Abdulkadir, I. Salaudeen, B. Shehu
In a natural gas field development plan, determining the life of the field and deciding the best-optimized production strategy as well as meeting the economic viability are the most important considerations to sustain gas production. Development optimization can increase the net present value by maximizing the hydrocarbon recovery and reducing the operating cost. Optimizing gas condensate bearing reservoirs below dew point exhibit complexities due to the hydrocarbon condensation and many times, an in-situ oil phase may result to reduce gas well productivity. Liquid loading can be a serious problem in gas-bearing condensate wells near the end of their production life. As the pressure in the drainage area is depleted below the dew point, the condensate will start to build up and the gas velocity in the production tubing falls below the critical rate resulting in inadequate energy to lift the entire condensate hydrocarbon out of the wellbore. The condensate liquid migrates down the tubing and accumulates at the bottom of the completion, increasing the bottom hole flowing pressure, thereby, reducing the production rate. Liquid loading phenomenon can be encountered in low productivity gas condensate wells. Preventive actions need to be considered for predicting and monitoring of liquid loading issue before it becomes a serious problem in production system form a reservoir to the surface facilities. This study focuses on optimizing gas production strategy in a field development plan of gas condensate well. Sensitivity analysis was implemented on the Bara well-1 through optimizing the operating parameters such as tubing sizes, wellhead pressures, skin factors, condensate gas ratio, water gas ratio and surface chokes sizes by using Niger-Delta field data and PROSPER dynamic simulator in order to select best well model construction that promote high gas deliverability and low condensate production. The reservoir GIIP has been estimated to 370 Bscf from both geological and dynamic simulation models. From the dynamic nodal analysis result, 5.5in tubing size promotes the highest optimum gas rate and low erosional velocity based on the investigated operating conditions.
{"title":"Production Optimization of Liquid Loading Problem in Offshore Niger Delta Gas Condensate Field","authors":"Mohammed Bashir Abdullahi, A. Sulaiman, U. Abdulkadir, I. Salaudeen, B. Shehu","doi":"10.2118/198873-MS","DOIUrl":"https://doi.org/10.2118/198873-MS","url":null,"abstract":"\u0000 In a natural gas field development plan, determining the life of the field and deciding the best-optimized production strategy as well as meeting the economic viability are the most important considerations to sustain gas production. Development optimization can increase the net present value by maximizing the hydrocarbon recovery and reducing the operating cost. Optimizing gas condensate bearing reservoirs below dew point exhibit complexities due to the hydrocarbon condensation and many times, an in-situ oil phase may result to reduce gas well productivity. Liquid loading can be a serious problem in gas-bearing condensate wells near the end of their production life. As the pressure in the drainage area is depleted below the dew point, the condensate will start to build up and the gas velocity in the production tubing falls below the critical rate resulting in inadequate energy to lift the entire condensate hydrocarbon out of the wellbore. The condensate liquid migrates down the tubing and accumulates at the bottom of the completion, increasing the bottom hole flowing pressure, thereby, reducing the production rate. Liquid loading phenomenon can be encountered in low productivity gas condensate wells. Preventive actions need to be considered for predicting and monitoring of liquid loading issue before it becomes a serious problem in production system form a reservoir to the surface facilities. This study focuses on optimizing gas production strategy in a field development plan of gas condensate well. Sensitivity analysis was implemented on the Bara well-1 through optimizing the operating parameters such as tubing sizes, wellhead pressures, skin factors, condensate gas ratio, water gas ratio and surface chokes sizes by using Niger-Delta field data and PROSPER dynamic simulator in order to select best well model construction that promote high gas deliverability and low condensate production. The reservoir GIIP has been estimated to 370 Bscf from both geological and dynamic simulation models. From the dynamic nodal analysis result, 5.5in tubing size promotes the highest optimum gas rate and low erosional velocity based on the investigated operating conditions.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84304665","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 reliability of dynamic simulation models can spell the difference between value creation or erosion during the development of a hydrocarbon reservoir. There is a strong need to use every available data during reservoir characterization, earth modelling and history matching of the production and pressure history of the reservoir. Of greater importance is the need to blind test the history-matched simulation model, to ascertain its reliability, especially when the model is to be used for further development of the reservoir. This paper details an offshore Niger Delta case study in which saturation logging results were used to blind test a history matched model, with an objective to further validate the model. The saturated oil reservoir was fully characterized using high resolution sequence stratigraphy and the earth model developed with available static data. History matching of the dynamic model was carried out using the parameter estimation approach, incorporating available dynamic data and tracking of contact movement observed in post-production wells. Following the history match, a saturation log was run in one of the producers in the reservoir, as a blind test for the quality of the history match. Results of the log matched the contacts in the dynamic model within 1 ft, in the subject well, providing additional confidence in the quality of the model. As a result, matched model has been used for the maturation of 2 new drill opportunities with significant estimated recoveries.
{"title":"Practical Deployment of Fluid Contact Tracking During History Matching for a More Reliable Reservoir Simulation Model: A Niger Delta Case study","authors":"F. Ogbuagu, Lynn Silpngarmlers","doi":"10.2118/198829-MS","DOIUrl":"https://doi.org/10.2118/198829-MS","url":null,"abstract":"\u0000 The reliability of dynamic simulation models can spell the difference between value creation or erosion during the development of a hydrocarbon reservoir. There is a strong need to use every available data during reservoir characterization, earth modelling and history matching of the production and pressure history of the reservoir. Of greater importance is the need to blind test the history-matched simulation model, to ascertain its reliability, especially when the model is to be used for further development of the reservoir.\u0000 This paper details an offshore Niger Delta case study in which saturation logging results were used to blind test a history matched model, with an objective to further validate the model. The saturated oil reservoir was fully characterized using high resolution sequence stratigraphy and the earth model developed with available static data. History matching of the dynamic model was carried out using the parameter estimation approach, incorporating available dynamic data and tracking of contact movement observed in post-production wells. Following the history match, a saturation log was run in one of the producers in the reservoir, as a blind test for the quality of the history match.\u0000 Results of the log matched the contacts in the dynamic model within 1 ft, in the subject well, providing additional confidence in the quality of the model. As a result, matched model has been used for the maturation of 2 new drill opportunities with significant estimated recoveries.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79493000","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}
Oguntade Tomiwa, Rotimi Oluwatosin, Ojo Temiloluwa, Olabode Oluwasanmi, I. Joy
Drilling fluids are the most important materials in drilling operations, therefore improving the properties of these fluids are very essential in order to meet up with the increase in demands and required standards. In this experimental study, Solanum tuberosum formulated biopolymer was used to improve the water based mud rheological properties and artificial neural network predicted data for (PV) plastic viscosity, (AP) apparent viscosity and (YP) yield point. Artificial neural network (ANN) was used to train the rheological properties of the formulated mud and the network developed predicted the rheological properties of an untrained combination of bentonite and modified biopolymer. The main target is to regenerate or predict the rheological properties of the formulated mud; (AP) apparent viscosity, (YP) yield point and (PV) plastic viscosity generated originally from experimental procedures but this time using the ANN. The mean average error target was set to around 5-10%. As a model for choosing the best ANN architecture for predicting target value, two statistical parameters, mean squared error (MSE) and correlation coefficient (R2) were utilized. A system with the lower estimations of MSE and the higher estimations of R2 gives more precise forecasts. Three different network were created and the two statistical parameters were used to determine the best number of neurons in the hidden layer. The developed neural network with best prediction has Root Mean Square Error (MSE) of 1.25221 and overall correlation coefficient (R2) of 0.99373 for the predicted plastic viscosity, yield point and apparent viscosity
{"title":"Improved Water Based Mud Using Solanum Tuberosum Formulated Biopolymer and Application of Artificial Neural Network in Predicting Mud Rheological Properties","authors":"Oguntade Tomiwa, Rotimi Oluwatosin, Ojo Temiloluwa, Olabode Oluwasanmi, I. Joy","doi":"10.2118/198861-MS","DOIUrl":"https://doi.org/10.2118/198861-MS","url":null,"abstract":"\u0000 Drilling fluids are the most important materials in drilling operations, therefore improving the properties of these fluids are very essential in order to meet up with the increase in demands and required standards. In this experimental study, Solanum tuberosum formulated biopolymer was used to improve the water based mud rheological properties and artificial neural network predicted data for (PV) plastic viscosity, (AP) apparent viscosity and (YP) yield point. Artificial neural network (ANN) was used to train the rheological properties of the formulated mud and the network developed predicted the rheological properties of an untrained combination of bentonite and modified biopolymer. The main target is to regenerate or predict the rheological properties of the formulated mud; (AP) apparent viscosity, (YP) yield point and (PV) plastic viscosity generated originally from experimental procedures but this time using the ANN. The mean average error target was set to around 5-10%. As a model for choosing the best ANN architecture for predicting target value, two statistical parameters, mean squared error (MSE) and correlation coefficient (R2) were utilized. A system with the lower estimations of MSE and the higher estimations of R2 gives more precise forecasts. Three different network were created and the two statistical parameters were used to determine the best number of neurons in the hidden layer. The developed neural network with best prediction has Root Mean Square Error (MSE) of 1.25221 and overall correlation coefficient (R2) of 0.99373 for the predicted plastic viscosity, yield point and apparent viscosity","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80347736","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. Acha, James Fadairo, Dennis Ogiesoba, Ikenna J. Okeke, Tata Emmanuel, Joseph A. Brown
The Nigerian Petroleum Development Company Limited (NPDC), and First Hydrocarbon Nigeria (FHN) its joint venture partner in Oil Mining Lease (OML) 26, established OML 26 Asset Management Team (AMT) to manage the lease together with all the assets and facilities on behalf of the joint venture (JV). The facilities include the flow station, the compressor station, and the gantry at Ogini field, a LACT Unit at Eriemu, and Operational Base for alternative evacuation in Asaba – Ase all in Delta State, Nigeria. Since the inception of the AMT in 2016, the asset has achieved and maintained a GOAL ZERO HSE performance record. Achieving GOAL ZERO HSE performance in a brown field like OML 26 is a herculean task. It is so, considering the operational challenges that require aggressive interventions in the forms of various projects, and the increased field activities. The debottlenecking activities results in more manhours, greater exposures and a high propensity for environmental impact. In line with the vision of the AMT to be the Best Performing Energy Investment (BPEI) the HSE department established a proactive system for identifying and managing operational risks and ensuring regulatory compliance consistently. This paper highlights the tremendous efforts, strategies and policies of the HSE department in ensuring success and meeting set HSE goals. It focuses on the costs in terms of commitments, governance and drive at all levels in achieving a sustained impressive performance and compliance to regulatory and international standards.
尼日利亚石油开发有限公司(NPDC)及其石油开采租赁(OML) 26的合资伙伴First Hydrocarbon Nigeria (FHN)成立了OML 26资产管理团队(AMT)，代表合资企业(JV)管理租赁以及所有资产和设施。这些设施包括Ogini油田的流量站、压缩机站和龙门台，Eriemu的LACT单位，以及尼日利亚三角洲州Asaba - Ase all的替代疏散操作基地。自2016年AMT启动以来，该资产已经实现并保持了GOAL ZERO的HSE绩效记录。在像oml26这样的棕色油田中实现GOAL ZERO HSE性能是一项艰巨的任务。考虑到需要以各种项目的形式积极干预的业务挑战，以及外地活动的增加，情况的确如此。消除瓶颈的活动导致更多的工时，更大的暴露和对环境影响的高倾向。为了实现AMT成为最佳能源投资(BPEI)的愿景，HSE部门建立了一个主动系统来识别和管理运营风险，并确保始终遵守法规。本文重点介绍了HSE部门在确保成功和实现既定HSE目标方面所做的巨大努力、战略和政策。它侧重于在实现持续的令人印象深刻的绩效和遵守法规和国际标准的所有层面上的承诺、治理和驱动方面的成本。
{"title":"Achieving & Sustaining Impeccable HSE Performance in Brown Field Operations Comes at a Cost OML26 Blazes the Trail","authors":"S. Acha, James Fadairo, Dennis Ogiesoba, Ikenna J. Okeke, Tata Emmanuel, Joseph A. Brown","doi":"10.2118/198801-MS","DOIUrl":"https://doi.org/10.2118/198801-MS","url":null,"abstract":"\u0000 The Nigerian Petroleum Development Company Limited (NPDC), and First Hydrocarbon Nigeria (FHN) its joint venture partner in Oil Mining Lease (OML) 26, established OML 26 Asset Management Team (AMT) to manage the lease together with all the assets and facilities on behalf of the joint venture (JV). The facilities include the flow station, the compressor station, and the gantry at Ogini field, a LACT Unit at Eriemu, and Operational Base for alternative evacuation in Asaba – Ase all in Delta State, Nigeria.\u0000 Since the inception of the AMT in 2016, the asset has achieved and maintained a GOAL ZERO HSE performance record. Achieving GOAL ZERO HSE performance in a brown field like OML 26 is a herculean task. It is so, considering the operational challenges that require aggressive interventions in the forms of various projects, and the increased field activities. The debottlenecking activities results in more manhours, greater exposures and a high propensity for environmental impact. In line with the vision of the AMT to be the Best Performing Energy Investment (BPEI) the HSE department established a proactive system for identifying and managing operational risks and ensuring regulatory compliance consistently.\u0000 This paper highlights the tremendous efforts, strategies and policies of the HSE department in ensuring success and meeting set HSE goals. It focuses on the costs in terms of commitments, governance and drive at all levels in achieving a sustained impressive performance and compliance to regulatory and international standards.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87895922","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. Onyeji, J. Mboto, T. Stafford, O. Ekun, G. Adamo, O. Oladimeji
The study area is characterized by sequences of sandstone and shale formations. Hydrocarbon production depletes the pore pressures within the sandstone reservoirs while the shale formations retain their original pressures. This leads to the narrowing of the safe mud weight window while drilling and increases the probability of the occurrence of wellbore stability issues such as loss circulation, tight spots, stuck pipe and hole collapse during drilling and casing run activities. Depleted reservoirs were traversed while drilling through the intermediate (12-1/4") hole section in GAB-7H. It was drilled with 9.0ppg equivalent mud weight (EMW) and an equivalent circulating density (ECD) of 9.6ppg EMW to the target depth. While running the 9 5/8" casing, it was observed that the wellbore had collapsed, thereby preventing the casing from getting to bottom of the hole which led to the abandonment of the hole section and a consequent side-track. This paper presents the lesson learnt and best practice that were adopted for GAB-7Hst and subsequent wells in the GAB field. Prior to the drilling of the sidetrack, a one-dimensional mechanical earth model (MEM) was constructed using petrophysical logs and formation tests of GAB-7H and other offset wells. Shale pore pressure was derived from gamma-ray, resistivity and sonic logs using the Eaton's and Bower's methods while sand pressures were measured/ estimated from modular dynamic testers (MDTs) and depletion models. The fracture gradient was derived using Matthew's and Kelly equation. Shear failure gradient was calculated using Modified Lade equations and log derived mechanical rock properties. The post-drill analysis of the offset wells was then calibrated with the drilling events and mud weights used. This revealed that the mud weight used to drill the 12-1/4" in GAB-7H was inadequate. An optimum mud weight program coupled with close monitoring of ECD is a key requirement to successful well construction in the GAB field, where several reservoirs at various states of depletion, sandwiched by shale formations are traversed. These has led to several successful drilling operations in the field.
{"title":"A Systematic Approach to Resolving Wellbores Stability Issues While Drilling through Depleted Sandstone Reservoirs, Case Study-GAB Field, Niger Delta","authors":"J. Onyeji, J. Mboto, T. Stafford, O. Ekun, G. Adamo, O. Oladimeji","doi":"10.2118/198813-MS","DOIUrl":"https://doi.org/10.2118/198813-MS","url":null,"abstract":"\u0000 The study area is characterized by sequences of sandstone and shale formations. Hydrocarbon production depletes the pore pressures within the sandstone reservoirs while the shale formations retain their original pressures. This leads to the narrowing of the safe mud weight window while drilling and increases the probability of the occurrence of wellbore stability issues such as loss circulation, tight spots, stuck pipe and hole collapse during drilling and casing run activities. Depleted reservoirs were traversed while drilling through the intermediate (12-1/4\") hole section in GAB-7H. It was drilled with 9.0ppg equivalent mud weight (EMW) and an equivalent circulating density (ECD) of 9.6ppg EMW to the target depth. While running the 9 5/8\" casing, it was observed that the wellbore had collapsed, thereby preventing the casing from getting to bottom of the hole which led to the abandonment of the hole section and a consequent side-track. This paper presents the lesson learnt and best practice that were adopted for GAB-7Hst and subsequent wells in the GAB field.\u0000 Prior to the drilling of the sidetrack, a one-dimensional mechanical earth model (MEM) was constructed using petrophysical logs and formation tests of GAB-7H and other offset wells. Shale pore pressure was derived from gamma-ray, resistivity and sonic logs using the Eaton's and Bower's methods while sand pressures were measured/ estimated from modular dynamic testers (MDTs) and depletion models. The fracture gradient was derived using Matthew's and Kelly equation. Shear failure gradient was calculated using Modified Lade equations and log derived mechanical rock properties. The post-drill analysis of the offset wells was then calibrated with the drilling events and mud weights used. This revealed that the mud weight used to drill the 12-1/4\" in GAB-7H was inadequate. An optimum mud weight program coupled with close monitoring of ECD is a key requirement to successful well construction in the GAB field, where several reservoirs at various states of depletion, sandwiched by shale formations are traversed. These has led to several successful drilling operations in the field.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76029861","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}
Lateef T. Akanji, J. Dala, K. Bello, Olafuyi Olalekan, Prashant Jadhawar
An enhanced neuro-fuzzy technique is deployed in production optimisation and fluid flow analysis for wells drilled and completed in Oredo oilfields Niger delta Nigeria. The impact of historical production data, reservoir rock and fluid properties, well geometry, architecture, completion profile and surface data on overall well deliverability is incorporated in the model. The artificial intelligence training process is complete at the point a minimum quantifiable error is obtained or when a value less than the set tolerance limit is reached. Production data obtained from the short and long-strings for wells completed in Oredo field was processed, analysed and input into the enhanced neuro-fuzzy algorithm. The adopted enhanced neuro-fuzzy system is capable of modelling the direct approach of Mamdani and that of Sugeno in a five-layer feed-forward neural network and fuzzy logic process designed and implemented in a C/C++ numerical computation objected oriented platform. This study highlights the significance of data analytics and artificial intelligence in well performance prediction and cost reduction and optimisation in oil producing wells.
{"title":"Application of Artificial Intelligence in Well Screening and Production Optimisation in Oredo Oilfields, Niger Delta, Nigeria","authors":"Lateef T. Akanji, J. Dala, K. Bello, Olafuyi Olalekan, Prashant Jadhawar","doi":"10.2118/198877-MS","DOIUrl":"https://doi.org/10.2118/198877-MS","url":null,"abstract":"\u0000 An enhanced neuro-fuzzy technique is deployed in production optimisation and fluid flow analysis for wells drilled and completed in Oredo oilfields Niger delta Nigeria. The impact of historical production data, reservoir rock and fluid properties, well geometry, architecture, completion profile and surface data on overall well deliverability is incorporated in the model. The artificial intelligence training process is complete at the point a minimum quantifiable error is obtained or when a value less than the set tolerance limit is reached. Production data obtained from the short and long-strings for wells completed in Oredo field was processed, analysed and input into the enhanced neuro-fuzzy algorithm. The adopted enhanced neuro-fuzzy system is capable of modelling the direct approach of Mamdani and that of Sugeno in a five-layer feed-forward neural network and fuzzy logic process designed and implemented in a C/C++ numerical computation objected oriented platform. This study highlights the significance of data analytics and artificial intelligence in well performance prediction and cost reduction and optimisation in oil producing wells.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"102 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77110808","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}
Odutola Toyin Olabisi, Ajienka Joseph Atubokiki, O. Babawale
Gas hydrate deposition is one of the major Flow Assurance problems in petroleum production in the offshore environment. Therefore, is important to accurately predict hydrate formation conditions and avoid these conditions or propose a hydrate management plan. This study compares the effectiveness of Artificial Neural Network (ANN) for predicting hydrate formation temperature to the effectiveness of other hydrate temperature prediction correlations such as: Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation. The ANN was trained using 459 hydrate formation experimental data points from Katz chart and Wilcox et al chart. Pressure (P) and specific gravity (ϒ) were chosen as the inputs in the 4-layer network while temperature was the output. The data points were for gases of specific gravity of 0.5539, 0.6, 0.7, 0.8, 0.9 and 1.0. The experimental pressures considered were from 49 psia to 4000 psia. The Neural Network was built using an excel add-in tool, NEUROXL. ANN accurately predicted the experimental hydrate formation temperature with the regression coefficient greater than 0.98 for the different specific gravities considered. Moreso, the error analysis shows ANN performed better than Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation because it had the least Mean Absolute percentage error, MAPE (3.5) compared to the other correlations. ANN is a viable tool for hydrate prediction and the current model can be improved upon by including more experimental data in the ANN.
天然气水合物沉积是海上油气生产中主要的流动保障问题之一。因此，准确预测水合物形成条件、避免水合物形成条件或提出水合物管理方案具有重要意义。本研究将人工神经网络(ANN)预测水合物形成温度的有效性与其他水合物温度预测相关性(如:Towler and Mokhtab相关性、Hammerschmidt相关性和Bahadori and Vuthalaru相关性)的有效性进行了比较。人工神经网络使用Katz图和Wilcox等人图中的459个水合物形成实验数据点进行训练。压力(P)和比重(y)被选为4层网络的输入，温度是输出。数据点适用于比重为0.5539、0.6、0.7、0.8、0.9和1.0的气体。实验压力范围为49psia至4000psia。神经网络是使用excel插件NEUROXL构建的。在考虑不同比重的情况下，人工神经网络准确预测了实验水合物形成温度，回归系数大于0.98。此外，误差分析表明，ANN比Towler和Mokhtab相关、Hammerschmidt相关和Bahadori和Vuthalaru相关表现更好，因为与其他相关相比，它具有最小的平均绝对百分比误差MAPE(3.5)。人工神经网络是一种可行的水合物预测工具，目前的模型可以通过在人工神经网络中加入更多的实验数据来改进。
{"title":"Artificial Neural Network for Prediction of Hydrate Formation Temperature","authors":"Odutola Toyin Olabisi, Ajienka Joseph Atubokiki, O. Babawale","doi":"10.2118/198811-MS","DOIUrl":"https://doi.org/10.2118/198811-MS","url":null,"abstract":"\u0000 Gas hydrate deposition is one of the major Flow Assurance problems in petroleum production in the offshore environment. Therefore, is important to accurately predict hydrate formation conditions and avoid these conditions or propose a hydrate management plan. This study compares the effectiveness of Artificial Neural Network (ANN) for predicting hydrate formation temperature to the effectiveness of other hydrate temperature prediction correlations such as: Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation. The ANN was trained using 459 hydrate formation experimental data points from Katz chart and Wilcox et al chart. Pressure (P) and specific gravity (ϒ) were chosen as the inputs in the 4-layer network while temperature was the output. The data points were for gases of specific gravity of 0.5539, 0.6, 0.7, 0.8, 0.9 and 1.0. The experimental pressures considered were from 49 psia to 4000 psia. The Neural Network was built using an excel add-in tool, NEUROXL. ANN accurately predicted the experimental hydrate formation temperature with the regression coefficient greater than 0.98 for the different specific gravities considered. Moreso, the error analysis shows ANN performed better than Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation because it had the least Mean Absolute percentage error, MAPE (3.5) compared to the other correlations. ANN is a viable tool for hydrate prediction and the current model can be improved upon by including more experimental data in the ANN.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"267 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91460457","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 study examines the levels of vehicular Carbon (IV) oxide (CO2) emissions in nine (9) selected locations characterised by high traffic congestion in Benin City Metropolis, Edo State, Nigeria. The contributory effects of these emission levels on climate change and air pollution were also assessed based on global standards. CO2 concentration measurements were conducted twice a day, four times a week, for a period of sixteen (16) weeks. Results showed that the highest average mean values were recorded at Ring Road, New Benin and Third East Circular Junctions with 1421 ppm, 1417ppm and 1171ppm respectively in the morning hours and 1767ppm, 1417ppm, 1217ppm respectively in the afternoon hours. Diurnal variations revealed significant statistical differences (P<0.05) for CO2 emissions generated at different times of the day. Spatial variations in the CO2 data were also statistically significant (P<0.05), with the highest mean concentrations of 1594ppm reported for Ring Road sampling station while New Benin and Five Junction sampling sites recorded mean CO2 emissions rates of 1417ppm and 745.8ppm respectively. The results showed that CO2 emission levels at these selected high traffic areas in Benin are approximately five times more than the internationally accepted safe limits of 350ppm for atmospheric CO2. However, these levels are less than the Occupational Safety and Health Administration (OSHA) permissible exposure limits of 5,000ppm. High vehicular exhaust emission which is the primary source of CO2 in the Benin city metropolis can be attributed to poor traffic handling and discipline; and low dilution and dispersion of the emitted CO2 due to prevalent low wind speeds in these study locations.
{"title":"Assessment of Vehicular Carbon Dioxide Emission at Major Road Intersections in Benin City, Edo State Nigeria","authors":"I. S. Iwuala, T. Oriaku","doi":"10.2118/198780-MS","DOIUrl":"https://doi.org/10.2118/198780-MS","url":null,"abstract":"\u0000 This study examines the levels of vehicular Carbon (IV) oxide (CO2) emissions in nine (9) selected locations characterised by high traffic congestion in Benin City Metropolis, Edo State, Nigeria. The contributory effects of these emission levels on climate change and air pollution were also assessed based on global standards. CO2 concentration measurements were conducted twice a day, four times a week, for a period of sixteen (16) weeks. Results showed that the highest average mean values were recorded at Ring Road, New Benin and Third East Circular Junctions with 1421 ppm, 1417ppm and 1171ppm respectively in the morning hours and 1767ppm, 1417ppm, 1217ppm respectively in the afternoon hours. Diurnal variations revealed significant statistical differences (P<0.05) for CO2 emissions generated at different times of the day. Spatial variations in the CO2 data were also statistically significant (P<0.05), with the highest mean concentrations of 1594ppm reported for Ring Road sampling station while New Benin and Five Junction sampling sites recorded mean CO2 emissions rates of 1417ppm and 745.8ppm respectively. The results showed that CO2 emission levels at these selected high traffic areas in Benin are approximately five times more than the internationally accepted safe limits of 350ppm for atmospheric CO2. However, these levels are less than the Occupational Safety and Health Administration (OSHA) permissible exposure limits of 5,000ppm. High vehicular exhaust emission which is the primary source of CO2 in the Benin city metropolis can be attributed to poor traffic handling and discipline; and low dilution and dispersion of the emitted CO2 due to prevalent low wind speeds in these study locations.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91299506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In today’s competitive business environment every effort and opportunity to improve a company’s capital projects process cycle needs to be explored. It is surprising that since we found oil in Nigeria, there has not been a functional and robust project historical database (owned by the industry) – containing key metrics on: cost, schedule, risk, scope, lessons learnt, etc. The IOCs have theirs but the owner-side is still yet to wake up! This article will propose a way to implement a National Project historical database (NPHDB) system for the Nigerian Oil and Gas sector. The collection of costs, schedules, resources, and technical data from completed projects can facilitate the development of benchmarks, ratios, factors, and other statistical analyses to measure and evaluate project performance and quality. Over time, if we introduce and properly use a project historical database system, individual project performance, as well as corporate decision making in choosing the best projects to pursue, will improve. The proposed National Historical Project Database system for the oil sector will assist the owner project management team (government agencies supervising the government interests in the oil and gas sector) to select the right projects to do in the first place, and then to properly execute the selected projects. The premise of this Paper is that the implementation of a project historical database system will allow the oil sector to improve their project processes in light of the total corporate capital budget and capital management efficiency. You can't improve if you don't know where you've been and how you got there. ‘The sooner we can get started capturing data the better; we shouldn’t let good projects pass us by’.
{"title":"Developing Historical Projects’ Database for Nigerian Oil & Gas Sector-Status, the Imperatives and the New Normal","authors":"J. O. Awoyomi","doi":"10.2118/198709-MS","DOIUrl":"https://doi.org/10.2118/198709-MS","url":null,"abstract":"\u0000 In today’s competitive business environment every effort and opportunity to improve a company’s capital projects process cycle needs to be explored. It is surprising that since we found oil in Nigeria, there has not been a functional and robust project historical database (owned by the industry) – containing key metrics on: cost, schedule, risk, scope, lessons learnt, etc. The IOCs have theirs but the owner-side is still yet to wake up! This article will propose a way to implement a National Project historical database (NPHDB) system for the Nigerian Oil and Gas sector. The collection of costs, schedules, resources, and technical data from completed projects can facilitate the development of benchmarks, ratios, factors, and other statistical analyses to measure and evaluate project performance and quality. Over time, if we introduce and properly use a project historical database system, individual project performance, as well as corporate decision making in choosing the best projects to pursue, will improve. The proposed National Historical Project Database system for the oil sector will assist the owner project management team (government agencies supervising the government interests in the oil and gas sector) to select the right projects to do in the first place, and then to properly execute the selected projects. The premise of this Paper is that the implementation of a project historical database system will allow the oil sector to improve their project processes in light of the total corporate capital budget and capital management efficiency. You can't improve if you don't know where you've been and how you got there. ‘The sooner we can get started capturing data the better; we shouldn’t let good projects pass us by’.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73468040","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}
Polymers are substances considered to have their molecular structure being built up majorly from a large amount of similar smaller units bonded together, for instance synthetic organic materials used as plastics and resins. It has been stated that due to reservoir heterogeneity and capillary forces affecting the water flood process causing low oil recovery after a particular interim, the technique of implementing polymer flooding as a chemical flooding mechanism has been introduced and is rapidly gaining attention in the oil and gas industry. The study describes primarily the formulation of a specific bio-polymer product and examining its performance in Enhanced oil recovery. The musa paradisiaca derived bio-polymer solution was formulated and injected at different concentrations by performing simultaneous experiments at certain operating conditions using a reservoir permeability testing equipment (RPT). Core sample flooding experiment was performed on three core samples of varying porosity and permeability. The bio-polymer is flooded through core sample at different pore volumes using the RPT as the flooding equipment. The bio-polymer solution has the ability to lower mobility by reducing the relative permeability to water as well as increasing its viscosity (an index of mobility ratio improvement), which reduces the mobility of the driving phase, hence causing an increased mobility of the driven phase(oil). From the experimental procedure performed in this study, the results indicate that the injection of newly derived bio-polymer solution into oil strata enhanced oil recovery where the incremental oil recovery attained for each core sample were 16.71%, 25.58% and 14.55% of the respective original oil in place (OOIP), indicating an average incremental oil recovery of about 18.94% for entire experiment performed. The results obtained from the flooding of newly derived bio-polymer solution proved a better performance in oil recovery percentage when compared with flooding of gum arabic polymer solution.
{"title":"Formulation of Bio-Waste Derived Polymer and Its Application in Enhanced Oil Recovery","authors":"A. Fadairo, G. Adeyemi, Obioma Onyema, A. Adesina","doi":"10.2118/198750-MS","DOIUrl":"https://doi.org/10.2118/198750-MS","url":null,"abstract":"\u0000 Polymers are substances considered to have their molecular structure being built up majorly from a large amount of similar smaller units bonded together, for instance synthetic organic materials used as plastics and resins. It has been stated that due to reservoir heterogeneity and capillary forces affecting the water flood process causing low oil recovery after a particular interim, the technique of implementing polymer flooding as a chemical flooding mechanism has been introduced and is rapidly gaining attention in the oil and gas industry. The study describes primarily the formulation of a specific bio-polymer product and examining its performance in Enhanced oil recovery. The musa paradisiaca derived bio-polymer solution was formulated and injected at different concentrations by performing simultaneous experiments at certain operating conditions using a reservoir permeability testing equipment (RPT). Core sample flooding experiment was performed on three core samples of varying porosity and permeability. The bio-polymer is flooded through core sample at different pore volumes using the RPT as the flooding equipment. The bio-polymer solution has the ability to lower mobility by reducing the relative permeability to water as well as increasing its viscosity (an index of mobility ratio improvement), which reduces the mobility of the driving phase, hence causing an increased mobility of the driven phase(oil). From the experimental procedure performed in this study, the results indicate that the injection of newly derived bio-polymer solution into oil strata enhanced oil recovery where the incremental oil recovery attained for each core sample were 16.71%, 25.58% and 14.55% of the respective original oil in place (OOIP), indicating an average incremental oil recovery of about 18.94% for entire experiment performed. The results obtained from the flooding of newly derived bio-polymer solution proved a better performance in oil recovery percentage when compared with flooding of gum arabic polymer solution.","PeriodicalId":11110,"journal":{"name":"Day 2 Tue, August 06, 2019","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90725217","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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