An Automated Workflow for Evaluating U.S. Onshore Shale Assets for Acquisition

Abstract Accurate and timely reserve evaluation is critical in the fast-paced environment of U.S. onshore shale asset acquisitions. Traditional workflows—often dependent on manual decline curve analysis, binning strategy to develop Type Well, and unavoidable subjective judgment—are labor-intensive, inconsistent, and prone to evaluator bias. This paper introduces an advanced data analytics system, Xcalibur, a fully automated, data-driven platform designed to streamline the technical due diligence process, enabling rapid, scalable, and repeatable asset evaluation. Xcalibur leverages machine learning, geostatistics, and Bayesian modeling to forecast production, generate statistically calibrated Type Well Profile (TWP), and compute technically recoverable resource (TRR) and economic metrics with quantified uncertainty. Key innovations include the use of the Transient Hyperbolic Model (THM) for production forecasting, integration of Markov Chain Monte Carlo (MCMC) for probabilistic decline curve analysis, and an automated method for identifying and modeling flowback ramp-up periods. The system also applies spatial database queries for offset well selection and rule-based filtering to ensure TWPs are both representative and unbiased. Validated across all the shale basins in U.S., Xcalibur demonstrates the ability to evaluate over 185,000 wells daily, enabling users to perform high-fidelity underwriting and economic screening efficiently—not weeks. The approach reduces reliance on manual intervention, increases technical consistency, and enhances decision confidence for investment. This paper outlines the core methodology, practical implementation, and impact of deploying this tool in acquisition and development planning.