Dynamic Evaluation of Flow Unit Based on Reservoir Evolution: A Case Study of Neogene Guantao Ng3 Formation in M Area, Gudao, Bohai Bay Basin

Abstract

To clarify the dynamic evolution characteristics of reservoir flow units during water injection development, the upper member of the Neogene Guantao formation in Block M of Gudao Oilfield is taken as a case study. Based on logging data, water injection profile test data, subwell data, and production performance data, among others, the flow zone index (FZI static) was proposed as the static evaluation parameter of the flow unit. The relationship between cumulative water injection (WT) and FZI change ( △ FZI ) was fitted. Hence, the △ FZI caused by water injection is combined with the static parameter of flow unit evaluation (FZI static) as the dynamic parameter of flow unit evaluation (FZI dynamic). The comprehensively evaluated reality of flow units in different periods is characterized by formula FZI static + △ FZI = FZI dynamic . The study shows that as the division standard of the flow unit, the FZI has a good correlation with the test results of the water absorption profile, such as water absorption intensity and relative water injection volume. Using the FZI as the static parameter of flow unit evaluation, four types of flow units were divided as follows: type I flow unit, FZI ≥ 4.1 ; type II flow unit, 4.1 > FZI ≥ 2.4 ; type III flow unit, 2.4 > FZI ≥ 1.7 ; type IV flow unit, 1.7 > FZI . The reservoir porosity and permeability characteristics of different flow units are highly correlated. Moreover, the relative permeability curve confirms that different flow units have different seepage capacities. Though, comparing characteristic reservoir parameters in different periods, the reservoir’s physical parameters became more conducive to fluid flow with the water injection development. The increase in the same water injection rate for type I and II flow units was greater than that for type III and IV flow units. Furthermore, when type I flow units were continuously distributed in a large area, high water consumption bands were formed, absorbing most of the water injection in the water injection wells. Hence, the waterflood efficiency was low. The change in different flow units was mainly controlled by the injection production well pattern and WT. Combined with the relative change characteristics of interlayer flow units, the changes can be divided into increasing type change and decreasing type changes. Finally, according to the distribution characteristics of different flow units, oil saturation, and water flooding results, strategies for tapping the potential of the remaining oil from three aspects (plane, interlayer, and inner layer) were formulated.

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

“…The methods for establishing the relationship between the FZI and logging curves include multiple linear regression and support vector regression (SVR) [28][29][30][31][32]. As the most used regression method, multiple linear regression is based on the least-squares method with empirical risk minimization as the criterion, while SVR is based on the linear kernel function and structural risk minimization as the criterion.…”