AI-Based Estimation of Hydraulic Fracturing Effect

Abstract

Summary 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.

Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem

“…Having data on production prior to refracturing makes the production forecast problem easier to solve, compared to the case of production forecast after primary fracturing operations. This has also been noted in other studies [10].…”

“…These analogue wells search is very useful for a petroleum engineer as it allows to analyse fracturing operations, conducted previously, and the design parameters values, check whether an operation was successful or not, etc. We can also extract additional features from the neighbouring wells, which increase predictive power of the models [10]. For example, in our work, we used features, such as average fluid production divided by distance from the pilot well, using wells within 1 km from the pilot one.…”

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

“…Nowadays, many IOR/EOR projects are being carried out worldwide. There are already examples of successful ML applications in the literature for hydraulic fracturing [17][18][19]. For such projects, it is crucial to assess the potential and risks in advance; however, this is not easy to do.…”

“…It proves itself to be robust to noise, immune to multicollinearity, and sufficiently accurate for engineering applications [28]. The selected models are currently the most popular for similar regression problems [11,17,19,[29][30][31].…”

“…A number of studies confirmed the effectiveness of ML models in application to oil recovery factor estimation [14][15][16]. Several other studies have reported the successful application of ML models to estimate the effects of hydraulic fracturing [17][18][19][20]. Kornkosky et al applied multivariate linear regression to estimate the waterflooding effect [11].…”