Integration of rock physics template to improve Bayes’ facies classification

Abstract

Reliable facies prediction is a key problem in reservoir characterization. Facies classification using an arbitrary selected zone is the simplest method. However, the problem is that the interpretation result strongly depends on the size of the selected zone. Using an RPT (rock physics template), we can define an accurate zone instead of defining an arbitrarily sharp cutoff for the zone. The next level of sophistication is using a statistical technique, whereby we can calculate not only the best zone, but also the probability of occurrence of that zone. Baye's theory is normally used for probabilistic facies classification.

A relationship between rock physics and nuclear magnetic resonance

“…The CO 2 properties as a function of temperature and pressure were derived based on data from Wang et al (2010), and brine properties were calculated from equations of Batzle and Wang (1992) In addition, rock physic and AVO modeling were done to predict rock properties from ultrasonic measurement. To predict rock properties from sonic data, we generated an RPT (Hossain et al, 2015) which combined multiple Figure 1: NMR measurement on fully saturated sample is compared to the NMR measurement after centrifuging at 100 psi. The cutoff time, which separates the T2 distribution into macroporosity and micro-porosity is defined as the relaxation time at the point where the cumulative porosity of the fully saturated sample equals the irreducible water saturation.…”

Rock physics based facies classification from seismic-inversion results in unconventional reservoirs

“…Defined facies are: silica-rich limestone, clay-rich limestone, lower kerogen-rich shale and higher kerogen-rich shale ( Figure 2b). We used an RPT (Figure 2a after Hossain et al 2015) for deterministic facies prediction. Furthermore, for seismic reservoir characterization, well data along with RPT are used to define the prior probability.…”

“…Predicted facies properties are important engineering inputs for drilling and production. For reservoir facies characterization, two different methods are commonly used: deterministic approach (Doyen, 1988;Loertzer and Berkhout, 1992;Avseth et al, 2005;Hossain et al 2015) and probabilistic approach (Gastaldi, et al 1998;Gouveia, 1996;Takashashi, 2000;Mukerji et al, 2001;Hossain and Mukerji, 2011;Grana et al, 2012;Hossain et al 2015). For deterministic facies classification we use an RPT workflow, while for probabilistic facies prediction, we can use Bayes' theory:…”

“…From this expression, we observe that the training facies influence the predicted facies, but the prior probabilities are more heavily influenced by the predicted facies. Hossain et al (2015) demonstrated the role of prior belief of Bayesian statistics by using three types of priors: uninformative priors, informative priors, and continuous priors and found that for uninformative prior, the posterior remains unchanged, while for informative priors, the posterior is increased. For uninformative priors, equation (1) becomes:…”