Well-Test for Leakage Detection Near Subsurface Faulted Reservoirs and Implications for Underground CO2 and H2 Geological Storage

ABSTRACT: Well-test is a formation evaluation method widely used worldwide because of its accuracy in estimating permeability, flow patterns, leakage detection, and reservoir boundaries identification. New analytical, numerical, and AI methods have been re-searched in recent years to save computational costs, providing more accuracy and supporting the energy transition, e.g., underground geological CO2 and H2 storage. This paper presents a new transient 2-D analytical model to compute the effect of the unequal for-mation thickness on the transient pressure drop during oil production near a partially communicating fault. The accentuated pressure drop may indicate leakage between the fault and the reservoir, which is undesirable for underground CO2 and H2 geological storage purposes. Sensitivity analysis is carried out to investigate this effect in some reservoir-fluid parameters, such as reservoir and fault permeability, porosity, and oil viscosity. The method developed combines the Laplace and Fourier transforms to compute the solution of 2 hydraulic diffusivity equations with source effect. The model developed also applies the image method to separate the pressure drop into an active region (real producer well) and an observation region (image well). The results also show that the total pressure drop, Δp, is modeled by the coupling between 2 erfc(u') and Ei(u') functions. The first function is coupled with the source raised from the real well (production well), and the second one is related to the image well (no source effect). A computational code was developed in Python, and the results were convergent. The main advantages of the proposed model are the ease of implementation in Python and quick run, which saves computational costs during the simulation. Moreover, the proposed model can be used for underground CO2 and H2 geological storage modeling and leakage detection near faults.