Bayesian Monte Carlo method for seismic predrill prospect assessment

Abstract

Predrill assessment of the probability that a potential drilling spot holds hydrocarbons (HC) is of vital importance to any oil company. Of equally great value is the assessment of hydrocarbon volumes and distributions. We have developed a methodology that uses seismic data to find the probability that a vertical earth profile contains hydrocarbons and the probability distribution of hydrocarbon volumes. The method combines linearized amplitude variation with offset (AVO) inversion and stochastic rock models and predicts the joint probability distribution of the combined lithology and fluid for the entire profile. We use a Bayesian approach and find the solution of the inverse problem by Markov chain Monte Carlo simulation. The stochastic simulation benefits from a new and tailored simulation algorithm. The computational cost of finding the full joint probability distribution is relatively high and implies that the method is best suited to the investigation of a few potential drilling spots. We applied the method to a case with well control and to two locations in a prospect: one in the center and one at the outskirts. At the well location, we identify the two reservoir zones and obtain volumes that fit the log data. At the prospect, we obtain significant increases in HC probability and volume in the center, whereas there are decreases at the outskirts. Despite the large noise components in the data, the risked volumes in the center changed by a factor of three. We have designed an algorithm for computing the joint distribution of lithology, fluid, and elastic parameters for a full vertical profile. As opposed to what can be done with pointwise approaches, this allows us to calculate success probability and HC volumes.

Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior

“…In Table 6 we have computed risked volume of HC for the three prior model choices. This Table also contains the results obtained from Kjønsberg et al (2010). Similar to Table 5, we see from the three first columns that the modeling of dependence in the prior model is essential to separate Profile A and B from C. The major difference in results in this paper and Kjønsberg et al (2010) is in profile B, where the risked volume is about half of what is predicted in Kjønsberg et al (2010).…”

“…In this case study we invert seismic data from three vertical profiles offshore Norway. We denote the profiles A, B and C. These are the same profiles and data as considered in Kjønsberg et al (2010). From the inversion we find facies probabilities and pore volume distributions.…”

“…This Table also contains the results obtained from Kjønsberg et al (2010). Similar to Table 5, we see from the three first columns that the modeling of dependence in the prior model is essential to separate Profile A and B from C. The major difference in results in this paper and Kjønsberg et al (2010) is in profile B, where the risked volume is about half of what is predicted in Kjønsberg et al (2010). The cause of this difference between the two models is that we have included two more failure LF‐classes in the current model, i.e.…”

“…A contribution of the current paper is that the more realistic Markov prior model is formulated such that the computational complexity drawing from the posterior distribution is not increased. The current work is along the lines of Kjønsberg et al (2010). In particular in terms of the goal of the analysis.…”

“…We apply the methodology on three earth profiles offshore Norway and discuss the impact of the correlation structure have on the volume distribution of real data. We also compare the results obtained by Kjønsberg et al (2010) both in terms of risk updates and performance of the Markov chain Monte Carlo algorithm.…”