Optimization of the Markov chain for lithofacies modeling: an Iranian oil field
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ORIGINAL PAPER
Optimization of the Markov chain for lithofacies modeling: an Iranian oil field Hanie Nikoogoftar & Behzad Mehrgini & Abbas Bahroudi & Behzad Tokhmechi
Received: 2 June 2013 / Accepted: 7 October 2013 # Saudi Society for Geosciences 2013
Abstract Reconnaissance and interpretation of underground heterogeneity, particularly lithofacies, always plays an important role in evaluation and management of hydrocarbon resources. Between various methods presented for modeling discrete characteristics of hydrocarbon reservoirs such as lithofacies, one with a more proper conformity with actual condition of reservoir facies is of great advantage. Formed on the basis of probability and presenting transition matrix, the Markov method is widely applied as a powerful tool for modeling the facies. In the present study, first, the method is introduced in details; then, in order to optimize it for conditions with insufficient well data, two suggestions are made based on changing the motion direction of the chain and increasing the conditional boundary in simulation procedure. The case study is a 12-km-long 110-m-thick section of anhydrite and three major members of the Asmari Formation from an oil field in southwest Iran. This section is modeled through Markov classical procedure, changing chain motion direction and finally adding one seismic horizon as another conditional boundary. The models set indicated that on the basis of using the data from three wells and seven
H. Nikoogoftar (*) Mining Engineering (Exploration), Faculty of Engineering, University of Tehran, Tehran, Iran e-mail: [email protected] B. Mehrgini Petroleum Exploration Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran A. Bahroudi Faculty of Engineering, University of Tehran, Tehran, Iran B. Tokhmechi Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahroud, Iran
seismic horizons, the best result with 86 % accuracy is for the state of using two conditional boundaries. Keywords Markov chain . Lithofacies . Transition matrix . Hydrocarbon reservoirs . Conditional simulation
Introduction Assessing underground heterogeneity, in particular reconnaissance of lithofacies, plays an important role in detailed exploration and evaluation of hydrocarbon reservoirs. This is why application of specific methods with highest capability and accuracy, capable of offering a clear and correct image of such heterogeneity (lithofacies) based on available data, is inevitable. So far, several approaches have been developed for quantitative interpretation of reservoir characteristics, which all can be categorized as probabilistic and deterministic methods. While for the deterministic methods it is impossible to calculate the accuracy of the model, the probabilistic methods can quantify the error and calculate the share of each data in optimizing the accuracy of the model gained (Avseth et al. 2005). Being named after the Russian mathematician Andre Markov, the Markov chain method is a probabilistic
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