Abstract
In this study, we propose a novel workflow to predict the production of existing and new multi-wells. To perform reliable production forecasting on heterogeneous shale formations, the features of these formations must be analyzed by classifying the formations into various groups; the groups have different production characteristics depending on the key factors that affect the shale formation. In addition, the limited data obtained from nearby existing multi-wells should be used to estimate the production of new wells. The key factors that affect shale formation were derived from the correlation and principal component analysis of available production-related attributes. The production of existing wells was estimated by classifying them into groups based on their production characteristics. These classified groups also identified the relationship between hydraulic fracturing design factors and productivity. To estimate the production of new wells (blind wells), we generated groups with different production characteristics and leveraged their features to estimate the production. Probabilistic values of the group features were entered into the input layer of the artificial neural network model to consider the variation in the production of shale formations. All the estimated productions exhibited less error than the previous analytical results, suggesting the utilization potential of the proposed workflow.
Highlights
In the ever-increasing global demand for energy, unconventional resources account for a large portion of untapped reserves
We developed a model using machine learning to classify groups using the key factors without going through a series of processes with different production characteristics of the shale formations
This enables the estimation of cumulative production per unit length for multiple wells; this can be used even in the case of oil wells that have been produced for a short period
Summary
In the ever-increasing global demand for energy, unconventional resources account for a large portion of untapped reserves. The application of hydraulic fracturing for efficient production and the number of horizontal wells is expected to increase These improvements in the production technology have led to low development costs and high commercial success rates. A reliable method for production forecasting that considers the unclear flow mechanisms during production and native and hydraulic design parameters of the shale formations is required. Data-driven analytics have been employed to predict the performance of shale formations using production-related attributes, such as native, design, and dynamic parameters. Several studies have shown that data-driven analytics are effective in estimating reservoir properties, predicting production, optimizing drilling, and solving various other issues in shale formations. Mohaghegh [6] identified the correlation among reservoir characteristics, rock-mechanical properties, hydraulic fracturing design factors, and production in shale formations, and evaluated their impact using pattern recognition.
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