An Improved Workflow for Shale Resource Assessment: Demonstrated Using Triassic Chang 73 Shale

Abstract In the context of energy structure transition, fossil fuels will continue to play a crucial role in the near- to medium-term future. Globally widespread shale deposits possess significant resource potential and have become key targets for energy exploration, amid growing challenges posed by the supply-demand imbalance of conventional petroleum and continuously rising energy consumption. Accurate assessment of shale oil potential, vital for informed exploration and decision-making, is still hampered by issues in hydrocarbon loss correction and resource classification criteria. This study addresses these core issues by proposing an innovative evaluation workflow that integrates geological data and optimization algorithms, using lacustrine shale samples from the third submember of the seventh member of the Triassic Yanchang Formation as an illustrative example for method validation. Our findings indicate that hydrocarbon loss significantly impacts the quality assessment of shale reservoirs. As thermal maturity increases, resource boundaries and oil retention threshold gradually decrease. This implies that shale reservoirs with high thermal maturity may demonstrate superior resource quality at similar levels of TOC and oil contents, making them favorable targets for exploration. Compared to existing classification schemes, the proposed method offers two major advantages. First, based on widely available pyrolysis data and incorporating hydrocarbon loss correction, it integrates quantitative characterization of multiple geological attributes, introduces geological boundary constraints, and employs genetic algorithm-driven iterative optimization to establish a rapid, reliable, and practical model for evaluating the resource potential of shale reservoirs. Second, the model systematically considers the influences of organic matter type on hydrocarbon generation, expulsion, retention, and mobility, with enhanced capability in quantitatively distinguishing between retained and movable hydrocarbons, thereby significantly improving its applicability and accuracy in shale reservoir assessment. This research presents an advanced data-driven and optimal-fitting strategy to define critical thresholds for oil content and mobility in shale resource evaluation. The results are expected to provide geoscientists and engineers working on shale resource systems with a robust methodological framework and procedural support.