A Novel Approach to Productivity Prediction of Carbonate Gas Reservoirs From Electrical Image Logs

Abstract

The production rate data and electrical image logs from 23 wells were used to set up an artificial neural network (ANN) model for the productivity prediction of carbonate gas reservoirs. The inputs of the ANN model are the attributes of weathering dissolution vugs, including connectedness, surface proportion, size, and thickness of vug zones. The ANN model was used to predict the gas production rates in two new wells. The predicted gas production rates separately are 70x10⁴ m³/d and 200x10⁴ m³/d, and the actual gas production rates are 101.97x10⁴ m³/d and 182x10⁴ m³/d. The approach provided satisfactory results.

Poor regression relationship between conventional openhole logs and production rate motivates maximizing the use of electrical image logs that provide details of the pore space of the reservoirs. Core observation and core laboratory analysis indicate that the pore space of the reservoirs mainly consists of vugs and fractures. Most of the fractures are weathered fractures with high angle, which are well-developed almost everywhere. The horizontal well trajectories perpendicular to northwest-southeast direction cut through more fractures, while the horizontal well trajectories parallel to northwest-southeast direction drill through fewer fractures.

Exactly identifying and quantifying the pore space of vugs is critical to the productivity prediction of the reservoirs. Normally, it is very difficult to use openhole logs to identify vugs. However, high-resolution electrical image logs are sensitive to the different types of vugs. With the help of the advanced processing techniques of the electrical images, it is possible to extract quantitative measures of important reservoir parameters from the electrical image logs. The connectedness of vugs serves as the permeability index, the surface area of vugs serves as the porosity index. The electrical image logs are thus used to delineate heterogeneous porosity and permeability of the carbonate gas reservoirs.

The case study demonstrates the quantification of the pore space of vugs from electrical image logs and the productivity prediction of the reservoirs by the ANN model and presents the relationship between well trajectory and natural fractures. In addition, the case study indicates that neural network is a good solution for the establishment of the complex multivariable relationships between the attributes of vugs and gas production rates, which is used for productivity prediction of the reservoirs.

AAPG Datapages/Search and Discovery Article #90323 ©2018 AAPG Annual Convention and Exhibition, Salt Lake City, Utah, May 20-23, 2018