Probabilistic reservoir characterisation using 3D pdf of stochastic forward modelling results in Vincent oil field

• Published in: Exploration geophysics (Melbourne) Vol. 51; no. 3; pp. 341 - 354
• Main Authors: Choi, Junhwan, Kim, Soyoung, Kim, Bona, Byun, Joongmoo
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.05.2020
• ISSN: 0812-3985; 1834-7533
• DOI: 10.1080/08123985.2019.1696151

Reservoir characterisation using a crossplot of elastic properties can be used to determine fluid and lithology in a seismic survey area. P-impedance and P-wave/S-wave ratios are commonly used as axis parameter for a 2D crossplot. To achieve this goal, the fluid and lithology must first be identified using well log data. However, when the well log data are too sparse, they cannot encompass the full suite of reservoir properties. Stochastic forward modelling (SFM) methods have been developed to overcome the sparseness problem. However, when the results of SFM are plotted on the 2D crossplot, the augmented data for different facies frequently overlap. To overcome this problem, we propose a probabilistic reservoir characterisation using 3D crossplotting of the SFM results. Axis-parameters of the 3D crossplot consist of the seismic attributes by which the facies are distinguished. The acoustic impedance (I p ), pseudo gamma ray (GR) log, and pseudo water saturation (S w ) log were used as the axis parameters of the 3D crossplot. To perform SFM, pseudo GR and pseudo S w log data must be expressed mathematically with well log data. Linear multi-regression analysis was used to derive the mathematical relationships of the different parameters. The probability distributions of the pseudo GR and pseudo S w logs were extracted using these relationships. Using the probability distributions of the I p , pseudo GR log, and pseudo S w log, the data were augmented by Monte Carlo simulation. The trivariate probability density function (3D PDF) of each facies was determined by the mean and covariance of the augmented data. The pseudo GR log and pseudo S w log volumes were extracted using a probabilistic neural network. Finally, a Bayesian inference was applied to calculate the facies probabilities using the 3D PDFs. We confirmed that the proposed method is more effective than the conventional reservoir characterisation method using 2D crossplot of SFM results.