Real-Time Formation Evaluation and Contamination Prediction Through Inversion of Downhole Fluid Sampling Measurements

Abstract

Abstract Acquisition of fluid samples using wireline formation testers (WFT) is an integral part of reservoir evaluation and fluid characterization. Recent developments in formation tester hardware have enabled wireline-based fluid sampling in a wide range of downhole conditions. However, accurate quantification of oil-based mud (OBM) filtrate contamination using data from downhole fluid analysis (DFA) sensors alone remains challenging, especially in difficult sampling environments and for advanced sampling tools which have complex inflow geometries and active guarding of filtrate flows. Such tools and conditions lead to contamination behaviors that do not follow simple power-law models which are commonly assumed in OBM contamination monitoring (OCM) algorithms. In this paper, we introduce a new OCM algorithm based on inversion of DFA data using a full 3D numerical flow model of the contamination cleanup process. Based on formation and fluid properties and operational tool settings, the model predicts the evolution of filtrate contamination as a function of time and pumped volume and can thus be used to forward-model the DFA sensor responses. Sensor data are then inverted in real time to provide contamination predictions. Real-time computation is enabled through fast, high-fidelity proxy models for the cleanup process. The proxy models are trained on and thoroughly vetted against a large number of full-scale numerical simulations. Compared to existing algorithms, the new OCM method is now applicable for all types of sampling hardware and a wider set of operating conditions. By directly relying on a model of the cleanup process, the physical properties of the formation and fluids (such as porosity, permeability, viscosity, and depth of filtrate invasion) are estimated during the inversion, thus providing additional valuable information for formation evaluation. The new method is demonstrated by practical application in both synthetic and field examples of oil sampling in OBM. The synthetic examples demonstrate the robustness of the algorithm and show that the true formation and fluid properties can be recovered from noise-corrupted sensor data. The field example presented demonstrates that contamination predictions are in good agreement with results from laboratory analysis, and the inverted formation properties are consistent with estimates based on openhole logs and pressure measurements.