Enhanced completion evaluations in unconventional reservoirs: new applications of fiber optic sensing and machine learning
In 2019, two horizontal wells were drilled in the Chalk Bluff field and equipped with fiber optic sensing technology to evaluate the completion effectiveness at each stage. The primary goal of these wells was to determine the optimal completion strategies in the field and the impact of nearby legacy wells. In recent years, fiber optic sensing and machine learning have shown potential to enhance completion evaluations in unconventional reservoirs. Therefore, we developed new methods using these technologies and applied them using data from the Chalk Bluff field to address the goals mentioned previously. We present a novel use of tube waves exited by perforation (or “perf”) shots and recorded on distributed acoustic sensing (DAS) to infer and compare the hydraulic connectivity of induced fractures near the wellbore on a stage-by-stage basis. We also discuss a new machine learning multivariate analysis workflow designed to identify the most correlated variables within complex, high-dimensional datasets. After validating the workflow’s effectiveness using a synthetic example, we applied it to Chalk Bluff data to identify significant correlations and provide recommendations for improving frac effectiveness and well performance while decreasing costs.
fiber optic sensing, machine learning, tube waves, fracture characterization, completion optimization, multivariate analysis
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Colorado School of Mines. Arthur Lakes Library