Comparison of Methods for Predicting Formation Permeability from Electrical Imaging Logging in Fractured Tight Reservoirs

Abstract

Permeability is of great importance in indicating formation filtration capacity and deliverability. Hence, it plays a key role in exploration and development wells evaluation. However, how to accurately predict reservoir permeability has become a key problem that has puzzled petrophysicists in the past few decades. The common methods, which are established based on multivariate statistics and widely applied, lose their role. The nuclear magnetic resonance (NMR)-based models, e.g., the Schlumberger Doll Research (SDR) center-based model and the Timur-Coates-based model, all cannot be well used due to the effect of saturated hydrocarbon or methane gas (CH4) to NMR response, especially in tight reservoirs due to the poor relationships among permeability and others parameters that caused by complicated pore structure. In addition, fractures play an important role in connecting intergranular pores and increasing permeability, whereas the common and NMR logging responses cannot well reflect this improvement. Since the birth of electrical imaging logging in the late 1980s, quantitatively characterizing fractured tight reservoirs is realized. In this study, to characterize the role of fractures in improving filtration capacity and permeability in fractured tight reservoir, the Palaeogene tight reservoirs in Huizhou Depression, eastern South China Sea Basin is used as an example, two new models of predicting permeability from electrical imaging logging are raised, and the reliability and accuracy are compared. In the first model, we extract two parameters from the porosity frequency spectrum, and they are defined as the logarithmic geometric mean value (φmv) and the golden section point variance (σg). Afterwards, we establish a relationship that connects formation permeability (K) with porosity (φ), φmv and σg. Based on this relationship, fractured tight reservoir permeability can be predicted from porosity frequency spectrum in the intervals with which electrical imaging logging is first acquired. In the second model, we improve the classical hydraulic flow unit (HFU) approach, and establish a new model to predict flow zone indicator (FZI) from electrical imaging logging to classify fractured formation. In these two models, all the involved coefficients are calibrated by using the experimented results of 118 core samples. Finally, these two models are extended into field applications to consecutively predict permeability from electrical imaging logging, and the predicted permeabilities are compared with core-derived results. Good consistency among them illustrates that the raised two models are all usable in our target Palaeogene fractured tight reservoirs in Huizhou Depression, especially the HFU-based model. It can be well used in all three kinds of formations. The average relative error between predicted permeabilities by using HFU-based model and core-derived results is only 14.37%. However, if the classical models are directly used in our target formations, permeability curve is underestimated.