A General Surrogate Model for CO 2 Flooding Dynamic Prediction Based on Dimensionless Features and Implicit Time

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High Resolution Image Download MS PowerPoint Slide In the development of CO 2 flooding reservoirs, complex multicomponent phase interactions and strong nonlinear flow behaviors cause the computational cost of traditional numerical simulations to increase significantly as the simulation period extends. This computational burden severely limits the efficiency of large-scale scheme optimization. To address this challenge, this paper proposes a general surrogate model based on the Transformer architecture, designed to achieve efficient and high-precision prediction of cumulative oil production and the CO 2 storage capacity. To tackle the generalization difficulty caused by differences in reservoir geological scales, a dimensionless feature system based on Pore Volume (PV) normalization was constructed. By converting absolute injection and production parameters into dimensionless numbers relative to the reservoir pore volume, this method effectively decouples the constraints of specific reservoir geometric scales, thereby establishing the physical universality of the model across different blocks. On this basis, by integrating an implicit time parameterization strategy with a Bayesian adaptive optimization algorithm, the model substitutes absolute physical time with cumulative fluid injection volume. This transformation converts discrete time-stepping prediction into a continuous mapping between development dynamics and the degree of injection. Validation experiments using 280,000 samples demonstrate that the model achieves R 2 values of 0.9986 and 0.9968 for oil production and CO 2 storage predictions, respectively. In terms of computational efficiency, unlike numerical simulations, where computational costs grow cumulatively over time, this model exhibits remarkable time independence. Taking a typical 5 year full lifecycle prediction task as an example, under the current computing configuration, the proposed surrogate model achieves a speedup of nearly 60 times compared with numerical simulations of equivalent precision. This characteristic, coupled with its excellent generalizability, effectively mitigates the computational bottleneck of long-cycle simulations, providing robust technical support for rapid assessment in CCUS-EOR development.

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