Data Analytics for Production Optimization in Unconventional Reservoirs

Mishra, Srikanta; Schuetter, Jared; Zhong, Ming; LaFollette, Randy F.

doi:10.15530/urtec-2015-2167005

Cited by 21 publications

(3 citation statements)

References 18 publications

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“…Due to the development characteristics of the shale reservoir (fast and intensive development process of numerous wells), it is impossible to obtain rock properties, porosity and permeability and reservoir pressure from all wells by performing both logging and well tests. Research was also carried out for the purpose of applying the coordinate well to replace or supplement geological factors [45][46][47]. In this study, a DNN model was developed that considers well direction and well status, which are categorical variables.…”

Section: Dataset Preparationmentioning

confidence: 99%

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Han

Kwon

2021

Energies

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Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data-driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi-layer perceptron was applied to predict the cumulative gas production as the dependent variable. The independent variable is largely divided into four types: well information, completion and hydraulic fracturing and production data. It was found that the prediction performance was better when using a principal component with a cumulative contribution of 85% using principal component analysis that extracts important information from multivariate data, and when predicting with a DNN model using 6 variables calculated through variable importance analysis. Hence, to develop a reliable deep learning model, sensitivity analysis of hyperparameters was performed to determine one-hot encoding, dropout, activation function, learning rate, hidden layer number and neuron number. As a result, the best prediction of the mean absolute percentage error of the cumulative gas production improved to at least 0.2% and up to 9.1%. The novel approach of this study can also be applied to other shale formations. Furthermore, a useful guide for economic analysis and future development plans of nearby reservoirs.

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Section: Dataset Preparationmentioning

confidence: 99%

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Han

Kwon

2021

Energies

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“…These data are challenging to obtain especially along the horizontal sections of the wellbore. Some researchers replaced these data with the location of the well (i.e., coordinates) as the mentioned properties are spatially changing (Mishra et al 2015;Wang and Chen 2019). Wang and Chen (2019) trained machine learning algorithms (RF, SVM, ANN, and AdaBoost) on 3160 horizontal well data of Montney unconventional formation to predict the first-year production and optimize the fracture design.…”

Section: Stimulationmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

101

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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“…In correlation analysis, simple statistical methods are used to explore the variables in the data set to establish the relationships that exit between each variable in the data set and to know the degree of signifi cance of each relationship among the variables (Schuetter et al 2015). In examining the relationship between the variables, a correlation containing two outputs was generated: (i) the correlation matrix which shows the coeffi cient of correlation between the variables as shown in Table 4 and (ii) the p-values which show the degree of signifi cance of the correlations as shown in Table 5.…”

Section: Correlation Analysismentioning

confidence: 99%

Application of machine learning models in predicting initial gas production rate from tight gas reservoirs

Amaechi

Ikpeka

et al. 2019

MGPB

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Driven by advancements in technology, tight-gas fi eld development has become a signifi cant source of hydrocarbon to the energy industry. The amount of data generated in the process is immense as most platforms are now being digitized. Machine learning tools can be used to analyse this data in order to build patterns between several dependent and independent variables. Forecasting initial gas production rates has important implications in the planning production/processing facilities for new wells, aff ects investment decisions and is an important component of reporting to regulatory agencies. This study is based on the analysis of reservoir rock/fl uid properties and selected well parameters to build decision-based models that can predict initial gas production rates for tight gas formations. In this study, two machine learning predictive models; Artifi cial Neural Network (ANN) and Generalized Linear Model (GLM), were used to determine the expected recovery rate of planned new wells. Production data was retrieved from 224 wells and used in developing the model. The results obtained from these models were then compared to the actual recorded initial gas production rate from the wells. Results from the analysis carried out revealed a Mean Square Error (MSE) of 1.57 on a GLM model whereas the ANN model gave an MSE of 1.24. Key Performance Index for the ANN model revealed that reservoir thickness had the highest (36.5%) contribution to the initial gas production rate followed by the fl owback rate (29%). The reservoir/fl uid properties contribution to the initial gas production rate was 53% while the hydraulic fracture parameters contribution to the initial gas production rate was 47%.

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Data Analytics for Production Optimization in Unconventional Reservoirs

Cited by 21 publications

References 18 publications

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs

A systematic review of data science and machine learning applications to the oil and gas industry

Application of machine learning models in predicting initial gas production rate from tight gas reservoirs

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