Integration of core, well logging and 2D seismic data to improve a reservoir rock model: a case study of gas accumulation in the NE Polish Carpathian Foredeep

Abstract

Geo log i cal mod els play a cru cial role in the de scrip tion and sim u la tion of fluid flow of both hy dro car bon-and wa ter-bear ing strata. Meth od ol ogy, based on the hy drau lic flow unit build on the ba sis of core plug data com bined with rock types de termined from logs and 3D seis mic cubes gen er ated on the ba sis of 2D seis mic sec tions is pre sented. It works as a pos si ble explo ra tion tool for the Mio cene gas ac cu mu la tions in the Carpathian Foredeep of Po land. De ter min is tic and sto chas tic, geostatistical meth ods were used to con struct a static res er voir model from 2D seis mic sec tions, lithological data and hydrau lic flow unit data. A pseudo-3D seis mic vol ume was gen er ated from all of the 2D seis mic data avail able, in or der to aid the mod el ling of hy drau lic flow units. This ap proach is ap pli ca ble to other res er voirs, where the avail abil ity of seis mic data is lim ited. This study dem on strates that even with out 3D seis mic data and with lim ited well log data, the pro posed hy drau lic flow unit ap proach can be suc cess fully ap plied to res er voir mod el ling through the in te gra tion of di verse data sets for a wide range of scales.Key words: hy drau lic flow units, res er voir static mod el ling, po ros ity, per me abil ity, well log ging, 2D seismics.

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

“…This approach works well if the data is spherical, multivariate and normally distributed [1]. Additionally, the cluttering of this method is good only if there is an equal number of data points in each population.…”

“…These techniques have been presented clearly by many researchers previously. They include Histogram, Probability plot and Ward's hierarchical [1,3,5] and the Global Hydraulic Elements (GHE) method [4,51].…”

“…Figure 5 shows the results of HU classification from each of the traditional methods described above: (a) A histogram of FZI does not clearly reveal the distribution of groups; (b) A normal probability plot illustrates how the local slope changes according to selected groups with a constant FZI cluster; the straight lines connecting the selected sections of the probability plot can determine some groups of HU; (c) based on the Ward's algorithm, the dendrogram clearly shows the FZI data clustering; (d) the GHE method proposed is a rapid and more straightforward approach to plot Φe and K on the pre-determined global hydraulic elements template. It can also divide core data into six groups [1,12]. Laboratory results (K, Φ e ) were combined with data from six logs including natural gamma ray intensity (GR), resistivity from LLD (short normal) and LLS (long normal), neutron porosity (NPHI), density (RHOB) and transit time interval (DT).…”

“…One of the most challenging problems in reservoir analysis is predicting reservoir permeability when data is limited. A traditional approach to predicting permeability uses simple linear regression between core porosity and permeability (LogK = aPHI + b), Linear Multiple Regression (LMR) and Alternating Conditional Expectation (ACE) [1,2].…”

“…Other studies tried to accomplish various supervised machine learning models for prediction reservoir characterization such as permeability, porosity, shear wave, and water saturation. Additionally, Linear Multiple Regression [1,11], Random Forest (RF) [12][13][14][15], Support Vector Machines [16,17], Boosted Tree and Artificial Neural Networks (ANN) were developed to improve predicting of FZI or permeability. Tang et al (2004) [14] used statistical methods for classifying electric log facies from awest African clastic reservoir.…”