Carbonate Karstified Oil Fields Geological Prediction and Dynamic Simulation Through Equivalent Relative Permeability Curves

Abstract

Karst systems heterogeneity may become a nightmare for reservoir modelers in predicting presence, spatial distribution, impact on formation petrophysical characteristics, and particularly in dynamic behaviour prediction. Moreover, the very high resolution required to describe in detail the phenomena does not reconcile with the geo-cellular model resolution typically used for reservoir simulation. The scope of the work is to present an effective approach to predict karst presence and model it dynamically. Karst presence recognition started from the analysis of anomalous well behaviour and potential sources of precursors (logs, drilling evidence, etc.) to derive concepts for karst reservoir model. This first demanding step implies then characterizing each cell classified as karstified in terms of petrophysical parameters. In a two-phase flow, karst brings to fast travelling of water which leaves the matrix almost unswept. This feature was characterized through dedicated fine simulations, leading to an upscaling of relative permeability curves for a single porosity formulation. The workflow was applied to a carbonate giant field with a long production history under waterflood development. Firstly, a machine learning algorithm was trained to recognize karst features based on log response, seismic attributes, and well dynamic evidence, then a karst probability volume was generated and utilized to predict the karst presence in the field. Karst characterization just in terms of porosity and permeability is sufficient to model the reservoir when still in single phase, however it fails to reproduce observed water production. Karst provides a high permeability path for water transport: classical history match approaches, such as the introduction of permeability multipliers, proved to be ineffective in reproducing the water breakthrough timing and growth rate. In fact, the reservoir consists of two systems, matrix, and karst: however, the karst is less known and laboratory analysis shows relative permeability only for the matrix medium. The introduction of equivalent or pseudo-relative permeability curves, accounting for both the media, was crucial for correct modelling of the reservoir underlying dynamics, allowing a proper reproduction of water breakthrough timing and water cut (WCT) trends. The implementation of a dedicated pseudo relative permeability curve dedicated to karstified cells allowed to replicate early water arrival, thus bringing to a correct prediction of oil and water rates, also highlighting the presence of bypassed oil associated with water circuiting, particularly in presence of highly karstified cells.

Adjusting Relative Permeability Measurements to History Match Water Production in Dynamic Simulation Models

“…Dynamic simulation models are under the focus of many recent researches. These researches considered various aspects of reservoir modelling as geological prediction through relative permeability curves (Tarantini et al, 2021) [1], modelling gas reservoirs and identification of two-phase flow by the effect of hydrate dissociation (Deng et al, 2022) [2] and the enhancement of oil recovery through low injection rate in low permeability reservoirs (Aslanidis et al, 2022) [3]. The goal of reservoir engineers is to build a countable model that mimics the actual behaviour of reservoirs.…”