Application of Fuzzy Logic for Determining Production Allocation in Commingle Production Wells

Abstract

For oil or gas fields with stratified reservoir layers, detailed productioncontribution for individual layer is always desired.Unfortunately, insome particular cases, production wells are completed following commingledscheme. This is worsened further if only very few production tests arerun for the field.This is the case for the Central Sumatera field withits 95 commingled production wells, among which only a few had undergoneproduction tests and none of them have ever undergone productionlogging.Problems rise when the occassion came in which detailedproduction contribution from individual reservoir layer is required for thefield's reservoir simulation modeling and productionevaluation/prediction. This paper presents an approach to solve the problem.The approach isbasically based on the application of soft computing (Fuzzy Logic) toinvestigate pattern of relationships between production contribution of layersin commingle wells and rock petrophysical data as well as other relevantgeological/engineering data.For the purpose, thirteen wells (key wells)that have production tests are assigned, among which three wells are assignedfor checking the validity of the recognised pattern.Using the validatedmost valid pattern, individual layer's production allocation for other wellsare determined with well-log analysis data as the major input. Result estimates for the candidate wells are better compared to resultsproduced by the conventional method of productivity index (PI)analogy.The resulted variation in water cut and separate oil and watersplit factors appear to be more realistic from any point of view. Introduction In managing a commingle production well, knowledge over productioncontribution of individual sand layer is always desired.The commonpractice performed during drilling and production activities of a productionwell is through the use of well testing/production testing and/or productionlogging. From the test, fluid dynamic data such as total liquid rate, water cut, and gas cut of an individual layer are produced.However, costand time efficiency is always used as the reason for not conducting suchtests. Therefore, even though such tests are always regarded as theprimary source of proof, an alternative means that can be used to provideestimates is always desired. Ideas of establishing a method that can provide illustration over productioncontribution of all layer(s) always exist.Certainly, there are approachesto serve the purpose such as productivity index (PI)/transmissibility analogyand petrophysical approach through fractional flow measurement in corelaboratory. However, those approaches are often considered inadequate foraccommodating various factors that may influence production contribution of aproductive layer. To materialize the requirement stated above, an indirect approach in theform of pattern recognition/modeling was taken.This approach was taken inorder to model relations between various factors in wellbore and productioncontribution of reservoir layers without being trapped by the certaincomplexity that may occur in any mathematical expressions trying to explain therelationships.For the purpose, fuzzy logic (a form of artificialintelligence) has been used.The choice is actually based on its capacityto accommodate both numeric and non-numeric data, since it is considered thatsome non-numeric data such as lithology and pore system also have someinfluence on production contribution.

Prospects of Intelligent Oil and Gas Fields

“…These problems include the intellectualization of analysis of large amounts of data collected from oil and gas fields, the intellectualization of drilling process, the forecast of reserves and optimization of oil and gas production, the optimization of the location and management of oil and gas fields, etc. For solving these and other problems, artificial neural networks [15][16][17][18][19], fuzzy logic [20][21][22][23][24], expert systems [25][26][27][28], machine learning methods [29], intelligent agents [30,31], genetic algorithms [32][33][34][35], data extracting methods [36,37], case-based reasoning -CBR [38][39][40], etc.…”

“…Fuzzy logic is used in the areas associated with oil and gas production technology. These areas include physics of oil reservoir [20,21], determination of oil and gas reserves [22], increasing oil and gas production [23], decision-making process [26], etc.…”

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

“…Fuzzy logic helps to guarantee precision and quality and to avoid inconsistency and uncertainty (Bilgen, 2010). Fuzzy logic applications in E&P operations have been reported in different areas such as engineering design and control (Nikravesh et al 1997;Sengul and Bekkousha 2002;Mohaghegh et al 2005;Widarsono et al 2005;Cao et al 2006;Taheri 2008;Weiss et al 2001;Zarei et.al. 2008;Murillo et al 2009) and also in operations of production facilities, systems and optimal well operations (Rivera and Farabee 1994;Dumans 1995;Xiong et al 2001;Alimonti and Falcone 2004;Garrouch and Lababidi 2005)…”

Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

“…An extensive list of such applications is presented in Table 1. In particular, many applications are focused on engineering-design and control problems (Sengul and Bekkousha 2002;Mohaghegh et al 2005;Widarsono et al 2005;Nikravesh et al 1997;Taheri 2008;Weiss et al 2001;Cao et al 2006;Zarei et al 2008;Wu et al 1997;Murillo et al 2009;Kanj and Roegiers 1999;Hajizadeh 2007b;Garrouch and Al-Ruhaimani 2003) relating to the operation of production systems and facilities. Neuroth et al (2000) applied fuzzy logic to the control of pump stations for oil-and gas-transport systems, and their method resulted in a reduction of the maintenance and operational costs.…”