456
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Monte Carlo Simulation of Oil Fields

, &
Pages 207-211 | Published online: 22 Sep 2006
 

Most investments in the oil and gas industry involve considerable risk with a wide range of potential outcomes for a particular project. However, many economic evaluations are based on the “most likely” results of variables that could be expected without sufficient consideration given to other possible outcomes, and it is well known that initial estimates of all these variables have uncertainty. The data is usually obtained during drilling of the initial oil well, and the sources are geophysical (seismic surveys) for formation depths and the areal extent of the reservoir trap, well logs for formation tops and bottoms, formation porosity, water saturation and possible permeable strata, core analysis for porosity and saturation data, and others. The question is how certain are the values of these variables and what is the probability of these values to occur in the reservoir to evaluate the possible risks? One of the most highly appreciable applications of the risk assessment is the estimation of volumetric reserves of hydrocarbon reservoirs (Monte Carlo). In this study, predictions were made about how statistical distribution and descriptive statistics of porosity, thickness, area, water saturation, recovery factor, and oil formation volume factor affect the simulated original oil in place values of two different oil fields in Turkey, and the results are discussed.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.