Fuzzy Ranking of Gas Exploitation Opportunities in Mature Oil Fields in Eastern Venezuela
Authors: Agbon I.S. , Aldana G.J. , Araque J.C. 
Published: December 2007

Increased demand for natural gas in Venezuela has led to an intensified search for additional supply from mature oil fields in Eastern Venezuela. The search has involved the ranking of oil reservoirs that could be converted to gas producing reservoirs. Unreliable reservoir and historical production data introduced high uncertainties to the ranking based on gas reserves only. A fuzzy model was therefore constructed and used for ranking 45 reservoirs. Eight input variables were used to evaluate each of the reservoirs. The variables included remaining petroleum reserves, remaining gas reserves, gas cap volume, oil production per psi, gas production per psi, flow capacity, storage capacity and the distance to existing gas transmission lines. Membership functions were developed for each variable and 20 fuzzy rules were used to capture the non-linear relationship amongst the input and output variables. The output variable was represented by 5 membership functions and presented in a 0 - 1 range after defuzzification. The reservoirs were then ranked on the basis of their output range values.

The results from the fuzzy model were compared to those obtained from a conventional methodology using a Decision Tree - Monte Carlo model. In this conventional model, the 8 input variables were normalized and represented as events in a decision tree. The variables were then weighted by using Monte Carlo simulation to generate the mean probability of each event occurring. A rolling netback calculation produced an output value for each reservoir. The output values were used to rank the 45 reservoirs. The paper concludes that although the two models handled the uncertainties of the impact of the 8 input variables on the output variable, the fuzzy model best capture the vagueness in the non-linear relationships between these variables.

Introduction

Venezuela has 147.5 TCF of gas reserves with 91% as associated gas and the remaining 9% as non-associated gas. In the Eastern part of the country, gas reserves are about 105 TCF with over 94% as associated gas. Gas demand is 1.92 BCF per day with a 7.5% annual growth rate. There is an on-going intensive search for gas to enhance the national gas supply. This search involved the conversion of mature oil reservoirs to gas producing reservoirs. Budget constraints limited the number of mature reservoirs that could be converted. Therefore, the reservoirs were ranked on the basis of Remaining Gas Reserves and the top ranked reservoirs were assumed to have the best gas opportunities. However, there was a high degree of uncertainty with the gas data. Furthermore, some of the reservoirs with large Remaining Gas Reserves had very low reservoir pressure and flow capacity. Others were located very far from any gas pipelines and surface infrastructure. There was therefore a need to use more sophisticated methods to rank the reservoirs.

Conventional ranking techniques include the Net Present Value (NPV), the Internal Rate of Return (IRR), the Expected Monetary Value (EMV), Value of Information (VOI) and hydrocarbon volume such as the Remaining Gas Reserves. 1 Moore and Tucker 2 used NPV and the Economic Chance Factor to rank exploration opportunities. Zammerilli 3 used the EMV to rank horizontal well locations in tight, naturally fractured reservoirs. Variables such as reservoir pressure, net thickness, success ratio, well length, drainage area, azimuth, well radius and topography were fixed at the beginning of the project. Variables like gas price and drilling costs were allowed to vary. Kelkar 4 also used EMV to rank independent projects in the presence of uncertainties. Demirmen 5 used Decision Tree and the Value of Information (VOI) analyses for screening and ranking subsurface appraisal projects. VOI captures uncertainties arising from volumetric parameters and hydrocarbon quality parameters. Gottardi et al 6 gave a good summary of the new conventional tools for ranking R & D projects using multivariate analysis.