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Keywords: machine learning
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Journal Articles
SPE Res Eval & Eng (2021)
Paper Number: SPE-200316-PA
Published: 16 November 2021
... with completion sensitivity performed on the Eagle Ford wells. They applied machine-learning techniques, including random forest and gradient boost, to create a proxy model for the optimal well completion design. Optimization of well completion is also widely considered in unconventional reservoir exploitations...
Journal Articles
SPE Res Eval & Eng 24 (04): 692–707.
Paper Number: SPE-206723-PA
Published: 10 November 2021
... 2021 Society of Petroleum Engineers artificial intelligence reservoir simulation machine learning hydraulic fracturing wellbore design reservoir geomechanics neural network structural geology fracture characterization reservoir characterization complex reservoir deep learning large...
Journal Articles
SPE Res Eval & Eng 24 (04): 827–846.
Paper Number: SPE-205493-PA
Published: 10 November 2021
... method hydraulic fracturing wellbore design heterogeneity reservoir characterization artificial intelligence drilling operation resolution variance correspond machine learning wellbore integrity reservoir simulation upstream oil & gas information high-resolution model stress solution...
Journal Articles
SPE Res Eval & Eng 24 (04): 923–939.
Paper Number: SPE-200581-PA
Published: 10 November 2021
... evolutionary algorithm asset and portfolio management reservoir simulation water injection rate optimization npv field development optimization and planning optimization level iteration machine learning artificial intelligence waterflooding multisolution framework optimization framework optimal...
Journal Articles
SPE Res Eval & Eng 24 (04): 780–808.
Paper Number: SPE-202700-PA
Published: 10 November 2021
... of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large...
Journal Articles
SPE Res Eval & Eng 24 (04): 940–951.
Paper Number: SPE-206718-PA
Published: 10 November 2021
...% in computing cost may be translated in practice into days of computations for just a single field and optimization run. 20 2 2021 18 5 2021 3 5 2021 3 8 2021 10 11 2021 Copyright © 2021 Society of Petroleum Engineers evolutionary algorithm optimization problem machine...
Journal Articles
SPE Res Eval & Eng 24 (04): 847–858.
Paper Number: SPE-205520-PA
Published: 10 November 2021
... with it is to use the gray-box approach that combines the strengths of physics-based models and machine learning (ML) used for dealing with certain components of the prediction where physical understanding is poor or difficult. However, the development of methodologies for the incorporation of physics into ML...
Journal Articles
SPE Res Eval & Eng 24 (04): 733–751.
Paper Number: SPE-206711-PA
Published: 10 November 2021
... bayesian inference shale gas pressure transient testing credible interval model parameter iteration machine learning flow in porous media drillstem testing upstream oil & gas posterior distribution convergence true value case 1 november 2021 complex reservoir algorithm scenario...
Journal Articles
SPE Res Eval & Eng 24 (04): 765–779.
Paper Number: SPE-205502-PA
Published: 10 November 2021
... the best predictors that are required to establish a rigorous model. 29 9 2020 25 3 2021 24 3 2021 14 5 2021 10 11 2021 Copyright © 2021 Society of Petroleum Engineers flow in porous media egypt upstream oil & gas correlation machine learning fluid dynamics...
Journal Articles
SPE Res Eval & Eng 24 (04): 809–826.
Paper Number: SPE-205188-PA
Published: 10 November 2021
... be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed...
Journal Articles
SPE Res Eval & Eng (2021)
Paper Number: SPE-208579-PA
Published: 09 November 2021
... and porosity values of 214 rock samples taken from North America and the Middle East. Using the values as grouping features, we used pattern-recognition algorithms in machine learning to cluster the original data into different groups. In each wetting phase saturation, we were able to quantify the gaps between...
Journal Articles
SPE Res Eval & Eng 24 (03): 514–522.
Paper Number: SPE-204217-PA
Published: 11 August 2021
... Copyright © 2021 Society of Petroleum Engineers enhanced recovery reservoir characterization artificial intelligence flow in porous media log analysis shale gas fluid dynamics relaxation region upstream oil & gas nmr response mixed-wet pore pore machine learning well logging complex...
Journal Articles
SPE Res Eval & Eng 24 (03): 536–551.
Paper Number: SPE-204477-PA
Published: 11 August 2021
... machine learning production control production monitoring artificial intelligence complex reservoir bayesian inference reservoir simulation sampler dca model probabilistic model metropolis gibb abc production history algorithm reservoir surveillance shale gas reserves evaluation...
Journal Articles
SPE Res Eval & Eng 24 (02): 367–389.
Paper Number: SPE-200462-PA
Published: 12 May 2021
... of state fluid dynamics machine learning miscible method gas injection method steam-solvent combination pvt measurement flow in porous media upstream oil & gas displacement co 2 diagram dispersion minimum miscibility pressure composition composition path may 2021 critical point spe...
Journal Articles
SPE Res Eval & Eng 24 (02): 310–324.
Paper Number: SPE-201542-PA
Published: 12 May 2021
..., which rely on using a frequency bandpass filter or waveform arrival-time separation to filter out the unwanted pipe mode, often fail when formation and pipe signals coexist in the same frequency band or arrival-time range. We hence developed a physics-driven machine-learning-based method to overcome...
Journal Articles
SPE Res Eval & Eng 24 (02): 358–366.
Paper Number: SPE-201635-PA
Published: 12 May 2021
...Tao Yang; Gulnar Yerkinkyzy; Knut Uleberg; Ibnu Hafidz Arief Summary In a recent paper, we published a machine learning method to quantitatively predict reservoir fluid gas/oil ratio (GOR) from advanced mud gas (AMG) data. The significant increase of the model accuracy compared to traditional...
Journal Articles
SPE Res Eval & Eng 24 (02): 325–340.
Paper Number: SPE-203384-PA
Published: 12 May 2021
...-height modeling using uncertain parameters. Because of the multitude of parameters, applying assisted-matching methods requires trade-offs regarding the quality of objective functions used for the various observed data. Applying machine learning (ML) in a Bayesian framework helps overcome...
Journal Articles
SPE Res Eval & Eng 24 (02): 341–357.
Paper Number: SPE-205354-PA
Published: 12 May 2021
... Engineers machine learning core analysis upstream oil & gas ct image artificial intelligence drilling operation classification pixel principal component core description reservoir characterization structural geology statistical feature information svm classifier massive fine-grained...
Journal Articles
SPE Res Eval & Eng 24 (01): 262–274.
Paper Number: SPE-203838-PA
Published: 10 February 2021
...Wendi Liu; Svetlana Ikonnikova; H. Scott Hamlin; Livia Sivila; Michael J. Pyrcz Summary Machine learning provides powerful methods for inferential and predictive modeling of complicated multivariate relationships to support decision-making for spatial problems such as optimization of unconventional...
Journal Articles
SPE Res Eval & Eng 24 (01): 219–237.
Paper Number: SPE-195438-PA
Published: 10 February 2021
... of Petroleum Engineers thermal method modeling & simulation artificial intelligence machine learning bayesian inference enhanced recovery optimization problem gas injection method posterior distribution objective function usa cumulative oil production hnp performance oil recovery...

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