Finding a Trend Out of Chaos, A Machine Learning Approach for Well Spacing Optimization

Abstract

Abstract Data-driven decisions powered by machine-learning methods are increasing in popularity when it comes to optimizing field development in unconventional reservoirs. However, since well performance is impacted by many factors (e.g., geological characteristics, completion design, well design, etc.), the challenge is uncovering trends from all the noise. By leveraging basin-level knowledge captured by big data sculpting, integrating private and public data with the use of uncertainty quantification, Augmented AI (a combination of expert-based knowledge and advanced AI frameworks) can provide quick and science-based answers for well spacing and fracking optimization and assess the full potential of an asset in unconventional reservoirs. Augmented AI is artificial intelligence powered by engineering wisdom. The Augmented AI workflow starts with data sculpting, which includes information retrieval, data cleaning and standardization, and finally a smart, deep and systematic data QC. Feature engineering generates all the relevant parameters going into the machine learning model—over 50 features have been generated for this work and categorized. The final step is to perform model tuning and ensemble, evaluating the model robustness, generating model explanation and uncertainty quantification. Augmented AI adopts an iterative machine learning modeling approach. This approach combines new and innovative engineering and G&G workflows with data-driven models so that a deep understanding of the field behavior can be developed. Loops from feature selection to model tuning are used until good model results are achieved. The loop is automated using Bayesians optimization. All machine learning models have different strengths and weaknesses for prediction. Instead of manually determining which machine learning model to use, this approach uses an adaptive ensemble machine learning approach that is a stacking algorithm that combines multiple regression models via a second level machine learning model. It smartly aggregates opinions from different models with reduced variance and better robustness. Augmented AI has been applied in unconventional reservoirs with great results. A case study in Midland Basin is presented in this paper. Domain-induced feature engineering was performed to obtain important features for predicting well performance, and initial feature selection was conducted using feature correlation analysis. A trusted and explainable ML model was built and enhanced with uncertainty quantification. After running several sensitivity analyses, Augmented AI optimized the attributes of interest, then vetted the outcome, generating a report and visualizing the results. In addition, further information about the direct impact of well spacing on EUR was deconvoluted from other parameters using an ML explanation technique for Wolfcamp Formation in Permian Basin and subsequently well spacing optimization was presented for the case study in Midland Basin. An innovative model was created using Augmented AI to optimize well spacing, leveraging big data sculpting, domain and physics-induced feature engineering, and machine learning. The learning was transferred from the basin model to the specific region of interest. Augmented AI provides efficient and systematic private data organization, an explainable machine learning model, robust production forecast with quantified uncertainty and well spacing and frac parameters optimization. Augmented AI models are already built for major basins such as Midland and Delaware basins. The learning and knowledge of the model can be transferred to any region in a basin and can be refined using more accurate private data. This allows conclusions to be drawn even with a limited number of wells.