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Digital Design And Computer Architecture

Different from the conventional oil reservoirs, the primary storage space of shale is micro/nano pore networks. Moreover, the multiscale and multi-minerals characteristics of shale also attract increasing attentions from researchers. In this work, the advances and challenges in the development of shale oil are summarized from following aspects: phase behavior, ﬂow mechanisms, reservoir numerical simulation and production optimization. The phase behavior of ﬂuids conﬁned in shale nanopores are discussed on the basis of theoretical calculations, experiments, and molecular simulations. The ﬂuid transport mechanisms through shale matrix are analyzed in terms of molecular dynamics, pore scale simulations, and experimental studies. The methods employed in fracture propagation simulation and production optimization of shale oil are also introduced. Clarifying the problems of current research and the need for future studies are conducive to promoting the scientiﬁc and effective development of shale oil resources. Cited as : Feng, Q., Xu, S., Xing, X., Zhang, W., Wang, S. Advances and challenges in shale oil development: A critical review. Advances in Geo-Energy Research, 2020, 4(4), 406-418, doi: 10.46690/ager.2020.04.06

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Advances and challenges in shale oil development: A critical review

\n We studied the applicability of a gradient-boostingmachine-learning (ML) algorithm for forecasting of oil and total liquid production after hydraulic fracturing (HF). A thorough raw data study with data preprocessing algorithms was provided. The data set included 10 oil fields with more than 2,000 HF events. Each event has been characterized by well coordinates, geology, transport and storage properties, depths, and oil/liquid rates before fracturing for target and neighboring wells. Each ML model has been trained to predict monthly production rates right after fracturing and when the flows are stabilized. The gradient-boosting method justified its choice with R2 being approximately 0.7 to 0.8 on the test set for oil/total liquid production after HF. The developed ML prediction model does not require preliminary numerical simulations of a future HF design. The applied algorithm could be used as a new approach for HF candidate selection based on the real-time state of the field.

AI-Based Estimation of Hydraulic Fracturing Effect

Dosage-based application versus ratio-based approach for metal- and plant-based coagulants in wastewater treatment: Merits, limitations, and applicability


 With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm.
 For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently outperforms on model testing and prediction accuracy. A database was constructed for a heterogeneous reservoir. Four widely used boosting algorithms were used on the database to predict the design only from the output of a short injection/falloff test.
 Feature importance analysis was done on eight output parameters from the falloff analysis, and six were finalized for the model construction. The outputs selected for prediction were fracturing fluid efficiency, proppant mass, maximum proppant concentration, and injection rate. Extreme gradient boost (XGBoost), categorical boost (CatBoost), adaptive boost (AdaBoost), and light gradient boosting machine (LGBM) were the algorithms finalized for the comparative study. The sensitivity was done for a different number of classes (four, five, and six) to establish a balance between accuracy and prediction granularity. The results showed that the best algorithm choice was between XGBoost and CatBoost for the predicted parameters under certain model construction conditions. The accuracy for all outputs for the holdout sets varied between 80 and 92%, showing robust significance for a wider utilization of these models.
 Data science has contributed to various oil and gas industry domains and has tremendous applications in the stimulation domain. The research and review conducted in this paper add a valuable resource for the user to build digital databases and use the appropriate algorithm without much trial and error. Implementing this model reduced the complexity of the proppant fracturing treatment redesign process, enhanced operational efficiency, and reduced fracture damage by eliminating minifrac steps with crosslinked gel.

Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications

Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast

Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractured completions, post-fracturing production analysis reveals that different stages produce very non-uniformly due to a combination of geomechanics and fracturing design factors. Hence, there is a significant room for improvement of current design practices. The workflow is essentially split into two stages. As a result of the first stage, the present paper summarizes the efforts into the creation of a digital database of field data from several thousands of multistage HF jobs on wells from circa 20 different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of a representative dataset of about 5000 data points. Each point in the data base contains the vector of 92 input variables (the reservoir, well and the frac design parameters) and the vector of production data, which is characterized by 16 parameters, including the target, cumulative oil production. Data preparation has been done using various ML techniques: the problem of missing values in the database is solved with collaborative filtering for data imputation; outliers are removed using visualisation of cluster data structure by t-SNE algorithm. The production forecast problem is solved via CatBoost algorithm. Prediction capability of the model is measured with the coefficient of determination (R^2) and reached 0.815. The inverse problem (selecting an optimum set of fracturing design parameters to maximize production) will be considered in the second part of the study to be published in another paper, along with a recommendation system for advising DESC and production stimulation engineers on an optimized fracturing design.

Victor M. Duplyakov

Papers

Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast

Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast