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Keywords: machine learning
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Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196572-MS
... in using machine learning methods for prediction of geomechanical properties such as the Poisson Ratio and Young Modulus properties from conventional well log data. This study shows that in the absence of dipole sonic logs, conventional log recordings can qualitatively indicate the brittleness of the...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196576-MS
... calibrating the estimates to the data. production forecasting diagnostic plot artificial intelligence data distribution inverse problem production data machine learning upstream oil & gas information regularization confidence interval standard deviation arp decline parameter probability...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196577-MS
.... Neural network models are trained to learn the key performance impacting factors on shale gas production in a dynamic manner, which could assist reservoir management decisions. complex reservoir artificial intelligence machine learning shale gas upstream oil & gas neural network model kpi...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196595-MS
...-established practice, a significant amount of analysis on their performance is focused on one or two key variables. The present paper adds to the existing body of literature by using data analytics and machine learning to evaluate this strategy from a truly multivariable standpoint. The paper also provides...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196613-MS
... play a significant role in determining the success of hydraulic fracturing treatments. We have also compared the performances of supervised machine learning algorithms in assessing the impact of rock properties on fracturing treatments. Such supervised machine learning algorithms can help integrate...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196608-MS
... predictions on well production. artificial intelligence neural network shale gas reservoir shale gas upstream oil & gas numerical simulation database cumulative gas production machine learning complex reservoir simulation mohaghegh spe reservoir scenario hydraulic fracturing neural...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196597-MS
... provided to achieve the required Q for any increase in YP/PV. machine learning drilling fluid chemistry well control wellbore pressure management annular pressure drilling pearson correlation particle turbulent flow data mining artificial intelligence drilling fluids and materials plastic...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196598-MS
... unconventional reservoir matrix form machine learning Upstream Oil & Gas deconvolution pressure response pressure map matrix permeability Artificial Intelligence Simulation training feature flow regime pressure drawdown leesn deconvolved response reservoir property noise permeability...
Proceedings Papers

Paper presented at the SPE Eastern Regional Meeting, October 15–17, 2019
Paper Number: SPE-196614-MS
... modifications are also presented. Both standard and machine learning techniques were used to analyze the results. neural network vertical permeability calculation machine learning reservoir simulation cutoff simulator flow in porous media fluid dynamics simulation heterogeneous model...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191823-18ERM-MS
... identify hidden patterns and help mitigate drilling challenges. Traditional data preparation and analysis methods are not sufficiently capable of rapid information extraction and clear visualization of big complicated data sets. Due to the petroleum industry's unfulfilled demand, Machine Learning (ML...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191827-18ERM-MS
... completion parameters? And how can we maximize the rate return on our investment? This study proposes innovative tools that allow researchers to answer these questions. We build these set of tools by utilizing the pattern recognition abilities of machine learning algorithms and public data from the...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191779-18ERM-MS
... such as numerical simulation, machine learning, and linear programming. The essence of field development and optimization is to use completions design, as well as well spacing, to optimize the net present value of the field based on current commodity pricing, capital expenditure, operating cost, cycle...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191785-18ERM-MS
... modeling machine learning Upstream Oil & Gas bottomhole gauge data child well fracture proppant Drillstem Testing communication marcellus well parent well Artificial Intelligence drillstem/well testing frac stage statistical analysis roc curve Completion Operation frac production...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191788-18ERM-MS
... that applies time series statistics to modeling and forecasting of well production rates. A few studies have applied time series statistics (ARIMA modeling) to production ( Ayeni and Pilat 1992 ; Ediger et al. 2006 ; Yusof et al. 2010 ). Machine learning offers another source of improvement beyond...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191793-18ERM-MS
..., bottom hole pressure, etc., giving operators a risk-based analysis of prospective sites. We also made this analysis available to the public in a user-friendly web app. machine learning Artificial Intelligence Upstream Oil & Gas flow rate production monitoring shale gas Duong Marcellus...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191794-18ERM-MS
... ridge regression interpretability machine learning regression correlation prediction exploratory plot annular pressure drop MSE dimensionality society of petroleum engineers predict pressure drop principal component pl regression principal component analysis predictive feature pearson...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191819-18ERM-MS
... concentration, fracturing fluid chemical additives, produced water reuse during completions, produced water chemistry, and geologic variation. Initial findings will be discussed and shared with lessons learned from production operations. machine learning Artificial Intelligence produced water discharge...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191806-18ERM-MS
... workflow. Artificial Intelligence principal component machine learning Upstream Oil & Gas shale gas principal component score Monte Carlo model statistical learning method tw area geologic dataset petrophysical property workflow complex reservoir HCA principal component analysis...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191796-18ERM-MS
... on Big Data initiatives. We discuss an ongoing initiative that employs cognitive analytics to generate production type curves via machine learning and couples the results with integrated economic analyses to guide field development. Challenges associated with data management, such as automated data...
Proceedings Papers

Paper presented at the SPE/AAPG Eastern Regional Meeting, October 7–11, 2018
Paper Number: SPE-191805-18ERM-MS
... have multiple implications for the reservoir engineer as well as for the understanding/benchmarking of the performance of fracturing techniques. A new stimulation indicator is proposed, 1_m2_clusters. natural language Drillstem Testing transition machine learning text classification Upstream...

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