New method for rock classification in carbonate formations using well-log-based rock fabric quantification

Challenges in rock classification of complex carbonate formations are often rooted in the failure to consider the spatial distribution of pore network or rock fabric. It is crucial to quantify rock fabric through different petrophysical properties that are affected by it. For example, Mercury Injection Capillary Pressure (MICP)-based pore typing uses the estimated pore-size distribution to cluster different pore types. Another example is well-log-based rock quality factor that takes advantage of the mud-filtrate invasion effects on well logs as proxy to the pore-size distribution. However, such methods require prior knowledge of the number of rock classes, which is mainly rooted in the differences between petrophysical rock classes and geological depositional facies. This thesis introduces a method for improving rock classification by determining the optimum number of rock classes, evaluating and quantifying pore network connectivity and geometry as rock fabric, as well as enhancing petrophysical evaluation. The proposed method involves an iterative procedure that starts with conventional well-log interpretation to obtain the petrophysical properties, such as volumetric concentration of shale, porosity, and permeability. Rock classification is performed using an unsupervised neural network with an initial assumption of the number of rock classes and well-log-based estimates of petrophysical and compositional properties as inputs. Permeability models are then developed in the pore-scale domain using core subsamples at different depths of interest. Models describing the correlation between electrical resistivity, conducting or effective porosity, and permeability are established in the pore-scale domain. These models are then applied in the log-scale domain to improve permeability estimates. Next, rock classification will be performed using the improved permeability estimates as inputs and updating number of rock classes. This process is repeated until a convergence in permeability estimates is achieved. outcomes of the iterative method include log-scale rock classification, permeability estimates, and the optimum number of rock classes. The method introduced in this thesis was successfully applied to two wells in a carbonate formation. outcomes of rock classification are in good agreement with the geological depositional facies. The iterative method results in 75% improvement in permeability estimates in the log-scale domain when compared against those obtained from conventional porosity-permeability correlations. Furthermore, this method effectively optimizes the number of rock classes, making it a promising approach for field cases with limited core measurements and no prior knowledge of rock types