Separation of productivity index zones using fractal models to identify promising areas of fractured reservoir rocks

Abstract

Identifying geological characteristics such as rock types and fractures is an important step in fractured reservoirs’ modeling and developing oil and gas fields. The productivity index (PI) is an essential parameter for this purpose. There are different methods for separating and identifying rock types and fractures, including simple statistical methods and complex fractal methods based on the spatial structure of the data. In this study, rock areas were isolated after modeling the PI parameter in a rock reservoir in southern Iran by ordinary kriging estimation. Then, the fractal concentration–area (C–A) and concentration–number (C–N) methods were used to classify the PI zones. The C–A fractal analysis revealed six different rock types and zones, and the C–N fractal method indicated four anomalies based on PI data in the studied reservoir rock. Based on the C–N and C–A models, the parts with PI ≤ 44 and PI ≤ 63, respectively, correspond to the production of wells from the reservoir rock matrix in this oil field and PI ≥ 223 include the production of wells at the fracture network of the reservoir rock. Fractal modeling indicates that the highest PI values occurred in the southeast and northwest parts of the studied oil field, suggesting better reservoir rock quality in this area. This problem is attributed to the presence of faults and the accumulation of fractures in these areas, which increases reservoir rock’s PI and permeability. The present study showed that multifractal methods are a very accurate method for separating all types of rock types in the reservoir and it separates things that are not visible in other methods such as petrophysical methods. The anomalies and communities identified for the PI parameter with these methods are well confirmed by geological evidence, especially the impact of fractures, faults and other diagenesis factors in the reservoir rock.