Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

Abstract

In this paper, we present an innovative factor analysis algorithm for hydrocarbon exploration to estimate the intrinsic permeability of reservoir rocks from well logs. Unlike conventional evaluation methods that employ a single or a limited number of data types, we process simultaneously all available data to derive the first statistical factor and relate it to permeability by regression analysis. For solving the problem of factor analysis, we introduce an improved particle swarm optimization method, which searches for the global minimum of the distance between the observed and calculated data and gives a quick estimation for the factor scores. The learning factors of the intelligent computational technique such as the cognitive and social constants are specified as hyperparameters and calculated by using simulated annealing algorithm as heuristic hyperparameter estimator. Instead of the arbitrary fixation of these hyperparameters, we refine them in an iterative process to give reliable estimation both for the statistical factors and formation permeability. The estimated learning parameters are consistent with literature recommendations. We demonstrate the feasibility of the proposed well-log analysis method by a Hungarian oilfield study involving open-hole wireline logs and core data. We determine the spatial distribution of permeability both along a borehole and between more wells using the factor analysis approach, which serves as efficient and reliable multivariate statistical tool for advanced formation evaluation and reservoir modeling.

Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters

“…In this research, the hydraulic conductivity values obtained from the Csókás method are correlated to the first factor. Accordingly, a strong negative nonlinear relationship with a correlation coefficient of − 0.84 is detected 69 (Fig. 13 a) that takes the following form, where a, b, c, and d represent site-specific regression coefficients.…”

Robust reservoir identification by multi-well cluster analysis of wireline logging data

“…For validating the results of cluster analysis, we use the permeability logs estimated earlier by factor analysis of raw wireline logs [ 47 ]. It was shown that the first statistical factor correlates highly to formation permeability calculated by Timur's equation [ 48 ] and sidewall core data in the same area.…”