An AI-based workflow for estimating shale barrier configurations from SAGD production histories

Zheng, Jingwen; Leung, Juliana Y.; Sawatzky, R. P.; Álvarez, Jose M.

doi:10.1007/s00521-018-3365-9

Cited by 26 publications

(9 citation statements)

References 47 publications

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“…Artificial neural networks, proposed by Rumelart and McClelland, have been widely used in the oil and gas industry as machine learning algorithms [31,[34][35][36]. Neurons in the network included an input layer, output layer and several hidden layers (Figure 5).…”

Section: Artificial Neural Network Algorithmmentioning

confidence: 99%

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Zhang

Ren²,

Feng

et al. 2021

Energies

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Refracturing technology can effectively improve the EUR of horizontal wells in tight reservoirs, and the determination of refracturing time is the key to ensuring the effects of refracturing measures. In view of different types of tight oil reservoirs in the Songliao Basin, a library of 1896 sets of learning samples, with 11 geological and engineering parameters and corresponding refracturing times as characteristic variables, was constructed by combining numerical simulation with field statistics. After a performance comparison and analysis of an artificial neural network, support vector machine and XGBoost algorithm, the support vector machine and XGBoost algorithm were chosen as the base model and fused by the stacking method of integrated learning. Then, a prediction method of refracturing timing of tight oil horizontal wells was established on the basis of an ensemble learning algorithm. Through the prediction and analysis of the refracturing timing corresponding to 257 groups of test data, the prediction results were in good agreement with the real value, and the correlation coefficient R2 was 0.945. The established prediction method can quickly and accurately predict the refracturing time, and effectively guide refracturing practices in the tight oil test area of the Songliao basin.

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Section: Artificial Neural Network Algorithmmentioning

confidence: 99%

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Zhang

Ren²,

Feng

et al. 2021

Energies

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“…The results of streamline simulation, such as generalized travel time, streamline density map, and oil production rates, are fast and reliable to replace conventional reservoir simulation [6,[40][41][42][43][44]. A proxy model, which is built to predict specific reservoir performances, has been used to predict pseudo dynamic data, such as the water breakthrough time and monthly steam injection rate [45,46].…”

Section: Combined Distancementioning

confidence: 99%

“…All clustering techniques, including K-means, K-medoids, and SOM, have difficulty in determining the appropriate number of clusters. Recently, there are various novel methods to automatically set the number of clusters: silhouette index, elbow criterion, cluster validity index, and Calinski-Harabasz index [20,39,46,47,66].…”

Section: = ( − )mentioning

confidence: 99%

“…Recently, some researchers have attempted to define a feature for oil sands according to well production data from full reservoir simulations [39,46]. Information for a well pair, oil production rates for a production well and the steam injection rate for the injection well were employed to calculate the dissimilarities for a thousand of 2D synthetic models [39].…”

Section: Unconventional Resources and Real Fieldsmentioning

confidence: 99%

“…They employed MDS two times to exclude redundant models among the 1000 models for effective clustering. Other researchers developed a proxy model by an artificial neural network to determine the location of the shale barrier by history matching [46]. The model can predict oil production according to the location of a shale barrier in oil sands reservoirs.…”

Section: Unconventional Resources and Real Fieldsmentioning

confidence: 99%

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Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

et al. 2019

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This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

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A data-driven reservoir simulation for natural gas reservoirs

Ghassemzadeh

Perdomo

Haghighi

et al. 2021

Neural Comput & Applic

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An AI-based workflow for estimating shale barrier configurations from SAGD production histories

Cited by 26 publications

References 47 publications

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

A data-driven reservoir simulation for natural gas reservoirs

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