Production Optimization Using Machine Learning in Bakken Shale

Luo, Guofan; Tian, Yao; Bychina, Mariia; Ehlig-Economides, Christine

doi:10.15530/urtec-2018-2902505

Cited by 40 publications

(14 citation statements)

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“…It also analyzes the impact on the quality of prediction made by the ANN technologies and the production of blind wells by implementing pattern recognition (Mohaghegh, S.D et al 2017). Luo et al (2018) proposed and also reported by Syed, F.I et al (2020Syed, F.I et al ( , 2021 in a couple of comprehensive review papers that petrophysical analysis using deviation in thickness, water saturation, and porosity. The study then applied an ANN to relate the first-year production to essential features (Luo et al 2018).…”

Section: Artificial Neural Networkmentioning

confidence: 75%

“…Luo et al (2018) proposed and also reported by Syed, F.I et al (2020Syed, F.I et al ( , 2021 in a couple of comprehensive review papers that petrophysical analysis using deviation in thickness, water saturation, and porosity. The study then applied an ANN to relate the first-year production to essential features (Luo et al 2018). Alabboodi and Mohaghegh (2016), proposes the use of ANN was used to find the relation of the trend between estimated ultimate recovery (EUR) and corresponding parameters.…”

Section: Artificial Neural Networkmentioning

confidence: 75%

“…They also determined that expensive fracture design proppants including ceramics and resin coated have no significant impact on well performance. In another literature, Luo et al (2018) implemented deep neural network to predict Bakken shale performance under large network of hydraulic fracture design parameters. They found proppant mass, and stage count highly impact production performances.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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AI/ML assisted shale gas production performance evaluation

Syed

Muther

Dahaghi

et al. 2021

J Petrol Explor Prod Technol

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Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.

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Section: Artificial Neural Networkmentioning

confidence: 75%

Section: Artificial Neural Networkmentioning

confidence: 75%

Section: Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

AI/ML assisted shale gas production performance evaluation

Syed

Muther

Dahaghi

et al. 2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…Gaswirth and Marra (2015) give a systematic presentation with displays of high-productivity areas based on the criteria considered by Theloy and Sonnenberg (2013). Luo et al (2018) found high-pressure areas are coincident with high initial gas production. These geologically favorable areas are clustered rather than randomly distributed throughout the extent of the middle member.…”

Section: Introductionmentioning

confidence: 85%

“…They applied several modeling approaches and found that water cut, volume of injected fluids per foot of lateral length (1 ft = 0.3048 m), and total organic content (TOC) are important predictors of well productivity. Luo et al (2018) used values of several geologic variables from interpolated maps that were generated from relatively few actual measurements to train a neural network predictive model that they assert enables operators to optimize production. Vankov et al (2017) constructed a three-dimensional geo-cellular model for the play used to predict values of oil-in-place from a very limited data set measuring porosity, thickness, and oil saturation.…”

Section: Previous Studies On Bakken Well Productivitymentioning

confidence: 99%

Growth Drivers of Bakken Oil Well Productivity

Attanasi

Freeman

2019

Nat Resour Res

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This paper identifies the drivers of the phenomenal growth in productivity in hydraulically fractured horizontal oil wells producing from the middle member of the Bakken Formation in North Dakota. The data show a strong underlying spatial component and somewhat weaker temporal component. Drivers of the spatial component are favorable reservoir conditions. The temporal component of well productivity growth is driven by increasing the number of fracture treatments and by increasing the volume of proppant and injection fluids used on a per fracture treatment basis. Random Forest, a nonparametric modeling procedure often applied in the context of machine learning, is used to identify the relative importance of geologic and well completion factors that have driven the growth in Bakken well productivity. The findings of this study suggest that a significant part of the well productivity increases during the period from 2010 to 2015 has been the result of improved well site selection. For the more recent period, that is, from 2015 through 2017, part of the improved well productivity has resulted from substantial increases in the proppant and injection fluids used per stage and per well.

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2022

Artificial Intelligence and Data Analytics for Energy Exploration and Production

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Production Optimization Using Machine Learning in Bakken Shale

Cited by 40 publications

References 0 publications

AI/ML assisted shale gas production performance evaluation

AI/ML assisted shale gas production performance evaluation

Growth Drivers of Bakken Oil Well Productivity

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