Many oil and gas reservoirs contain laminated and/or bioturbated facies. In such cases, conventional petrophysical analysis tends to underestimate reservoir properties resulting in pessimistic in-place volumes estimation and overlooked potential. The objective for incorporating the characterization of bioturbated and laminated facies is to build an anisotropic petrophysical spatial properties distribution describing the static model reservoir plumbing system and yielding practical reservoir simulations to improve production predictions, enhance sweep efficiency and maximize oil recovery.
The approach is based on characterization of heterolithic facies utilizing vertical resistivity (RV) and horizontal resistivity (RH) to provide heterolithic facies tagging, the calibrated macro-micro petrophysical properties, the correlation of RV/RH to KV/KH, the proportion of macroporosity-microporosity and burrow density as the inputs for static modeling. A high density permeametry and core Dual Energy Computed Tomography (DECT) Scan were used as reference to restore facies proportion and maintain reservoir heterogeneity as the impact on scaling factor in building static model.
This Seligi field example demonstrates that low-resistivity pay heterolithic facies remain untapped due to underestimated reservoir properties from a conventional petrophysical approach and illustrates complex flow pathways created by different sweep patterns under dynamic conditions.
From a practical engineering perspective, the heterolithic facies collocate with development uncertainties associated with erratic patterns of flow pathway. Field development opportunities for targeting this type of facies and the challenge for efficient hydrocarbon depletion with more accurate prediction of the spatial configuration and connectivity of high performance are ongoing.
As technology develops to understand reservoir complexity, more published documentations related to heterolithic facies characterization are becoming available in the industry; however, the idea of how to incorporate those characterization results into 3D numerical integration is still very limited. This paper provides an alternative solution for practical integration, both for static and dynamic simulation models, to support management decisions to optimize reserve recovery and maximize NPV.