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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207122-MS
... Abstract 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...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207088-MS
... disposal produced water re-injection reservoir petroleum engineer injectivity machine learning produced water downhole produced water disposal reduction permeability suspended solids SEPLAT currently operates 4 OMLs; 4, 38, 41 and 53. At the time of acquisition of these assets (11years...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207083-MS
... a critical review of current understanding and recent efforts in the estimation of gas deviation factor. artificial intelligence compressibility factor reservoir simulation deviation pvt measurement correlation van der waal petroleum engineer machine learning upstream oil & gas prediction...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207117-MS
.... Therefore, structural control, facies type, reservoir thickness and nature of oil volatility are key forces driving the gravity drainage mechanism. machine learning log analysis well logging reservoir characterization flow in porous media neural network structural geology history matching...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207079-MS
... the oil production volume. Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207111-MS
... characterization log analysis well logging upstream oil & gas machine learning wellbore integrity structural geology wellbore design strength niger delta wave velocity reservoir geomechanics artificial intelligence tri-axial test correlation uniaxial compressive strength unconfined compressive...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207161-MS
... coefficients which it then inserts into an empirical formula to predict ROP. This model was modified using non-linear curve-fitting to estimate the coefficients and make it reduce bias to generalize better. Machine learning models such as Gradient Boosting, Random Forest, ANN, and DNN were used...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207139-MS
... quality index. machine learning air emission us government novel approach artificial intelligence eastern nigeria upstream oil & gas oil and gas production natural gas gaseous emission efficiency combustion emission gaseous pollutant ambient air quality parameter flowstation...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207129-MS
... in the petroleum industry. machine learning upstream oil & gas assessment artificial intelligence artificial intelligence model artificial neural network mse neural network input parameter aapre ann model dataset output parameter reservoir mean square error permeability formation damage...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207145-MS
... in assisting operators in gas performance reporting, production allocation and flare penalties where applicable upstream oil & gas software technology machine learning accuracy regulatory requirement measurement uncertainty artificial intelligence flare gas measurement penalty petroleum...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207135-MS
... remaining work required to progress EOR process in this marginal oil field. chemical flooding methods eor process application enhanced recovery consideration machine learning upstream oil & gas implementation integrated eor screening petroleum engineer artificial intelligence workflow...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207153-MS
... risks. This body of information can serve as a guideline for adopting AI in the oil and gas industry. A trend of industry-tailored intelligence solutions would be more effective in the evolving energy industry. machine learning reservoir simulation data mining upstream oil & gas...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207163-MS
... Abstract Several computer-aided techniques have been developed in recent past to improve interpretational accuracy of subsurface geology. This paradigm shift has provided tremendous success in variety of Machine Learning Application domains and help for better feasibility study in reservoir...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207152-MS
..., 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...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207185-MS
... drilling fluid management & disposal upstream oil & gas drilling fluid drilling fluids and materials machine learning artificial intelligence petroleum engineering engineering eqn fracture support vector machine principal component analysis lost circulation petroleum...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207193-MS
... Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-208234-MS
... Abstract 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...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-208246-MS
.... Hence, the results of the analysis can help us to better target wells that are potential candidates for high water cut, high WOR, High gas rates and low oil rates. production control machine learning data mining reservoir surveillance intervención de pozos petroleros artificial intelligence...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-207206-MS
.... This yielded a value of about 6%, implying a 94% chance of correctly predicting decommissioning cost. us government machine learning upstream oil & gas subsea system offshore facility decommissioning artificial intelligence platform information decommissioning project dataset accuracy...
Proceedings Papers

Paper presented at the SPE Nigeria Annual International Conference and Exhibition, August 2–4, 2021
Paper Number: SPE-208450-MS
... 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...

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