Development of one dimensional geomechanical model for a tight gas reservoir

Abstract

Estimating rock-mechanical, petrophysical properties and pre-production stress state is essential for effective reservoir planning, development, and optimal exploitation. This paper attempts to construct a comprehensive one-dimensional mechanical earth model (1D MEM) of the Mandapeta gas reservoir of Krishna Godavari (KG) basin, India. The methodology comprises a detailed stepwise process from processing and analysis of raw log data, calibration of log-derived dynamic properties with static ones using regression models developed from tested core samples, and final rock mechanical property estimation. Pore pressure profiles have been estimated and calibrated with the Repeat formation tester (RFT) data for every thirty-five wells. Overburden and horizontal stresses have also been evaluated and calibrated using data from the Leak-off Tests (LOT) or Extended Leak-off Tests (XLOT). A menu-driven program is developed using PYTHON code for visualization and on-time revision of 1D MEM. The resulting comprehensive 1D MEM predicts and establishes the rock-mechanical properties, pore pressure, and in-situ stress values of the basin. Besides its use in planning future wells, development of the field, and yielding insight into the various well challenges, it can also be used to develop a 3D MEM of the reservoir.

Prediction Technology of a Reservoir Development Model While Drilling Based on Machine Learning and Its Application

“…Wang Deying et al extensively employed various experimental methods, including core analysis, X-ray diffraction, scanning electron microscopy, conventional physical properties testing, zircon dating, mineral dissolution simulation, statistical analysis methods to investigate the geological characteristics, genetic mechanisms, and a development model of gneiss weathering crust reservoirs in the Bohai Sea [2,10,11]. Shenwei et al successfully achieved qualitative and quantitative characterization of each phase zone by integrating core samples, rock flakes, conventional logging data, imaging logging data, and array acoustic logging data [12][13][14]. Although these approaches are comprehensive in nature, they rely on post-drilling logging data and coring experiment results, which may not fulfill the requirement for effective operational decision-making [15].…”