Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques

Rock mechanical parameters of reservoir rocks play an extremely important role in solving problems related to almost all operations in oil or gas production. A continuous profile of these parameters along the depth is essential to analyze these problems which include wellbore stability, sand production, fracturing, reservoir compaction, and surface subsidence. The mechanical parameters can be divided into three main groups, viz., elastic parameters, strength parameters, and in-situ stresses. Even the profile of in-situ stresses with depth is estimated using logs with elastic parameters as an essential input. The focus of this work is on the prediction of elastic parameters and their variation with the depth of a given reservoir. For an isotropic medium, there are two independent elastic parameters, viz., Young's modulus and Poisson's ratio. Generally, logging data consisting of density, compressional and shear wave velocities are used to estimate these parameters. However, these data provide dynamic elastic properties which are different from static values, especially in case of Young's modulus. To get continuous rock samples throughout the depth of the reservoir and conduct triaxial tests to determine the static values of these parameters is extremely expensive. Consequently, static values of Young's modulus and Poisson's ratio obtained from laboratory testing on rock samples acquired from selected intervals are used to calibrate the dynamic data obtained from logs. However, since the rock layers vary in their properties with depth, a realistic estimation of static elastic values of the rock is still a challenge. The problem is more prominent in limestone rocks compared to sandstone rocks. Further, shear velocity data is not always available from well logs, making the problem more difficult. An extensive experimental program was carried out first to obtain the static values of elastic parameters of reservoir rock samples at reservoir conditions of high pressure. Log data consisting of different variables such as density, velocity, and porosity from the same wells were also obtained. Three artificial intelligence methods viz. Neural Network, Fuzzy Logic and Functional Network, were used to obtain a continuous profile of static elastic parameters along the depth. The results obtained from these approaches were compared using log inputs. The strengths of each of these approaches are also discussed.