Identification of Horizontal Well Placement Using Fuzzy Logic

Abstract

Estimated to have over 3.5 billion barrels of original oil in place, the 114 year old Kern River Oil Field was a sleeping giant until the late 1960's when steamflooding began and oil production ramped up from 19,000 to over 142,000 BOPD by the mid 1980's. Kern River Field is now in decline, but still produces over 70,000 BOPD, making it one of the largest onshore oil producers in the continental United States. In 2007 the field's cumulative production reached two billion barrels of heavy oil. With the introduction of horizontal wells in 2007, the production decline curve that had been at approximately 6%, began to flatten and today the decline rate is at 2%. Since the discovery of the field, over 19,000 wells have been drilled with the largest producers, horizontal wells, drilled in the last 6 years.Though the oil production is in decline, new opportunities are continuing to be identified and exploited using a broad array of reservoir management tools. The primary tool utilized for both tactical and strategic planning is a full field 3D reservoir model. The static lithologic framework for this full field model uses over 12,500 normalized resistivity logs. Temperature, steam and oil saturation data from a network of over 650 surveillance wells are periodically incorporated into this model to provide current reservoir conditions. Project teams first identify their opportunities in the full field model using a filtering criteria. For example, horizontal infill wells seeking "hot bottom oil" within partially depleted reservoir intervals can be developed around geo-body criteria including property cut-off values for gas saturation, oil saturation, temperature, resistivity and thickness.The filtering criterion was useful in identifying well locations; however, the pool of candidates significantly declined due to the exclusive nature of the hard cut-off approach. The lack of candidates threatened the continued horizontal drilling program. A Fuzzy Logic approach was considered to deal with the imprecise information and aggregate multiple variables with suitable membership functions. The Fuzzy Inference System (FIS) using the Mamdani model incorporated knowledge rules developed by the asset team. Once developed, the system was run for each grid block of the nine productive formations of the field, generating areas of interest for identification and placement of horizontal wells.The output of the system allowed generation of "quality maps" which revealed target areas with substantial potential of horizontal drilling. Moreover, some of these target areas were not even considered in previous searches. In a first pass, approximately 600 new locations were identified.The paper presents a new approach for identification and placement of horizontal wells using fuzzy logic. In addition to the large number of wells identified, the methodology focused the asset team on high potential areas, as well as offered an objective approach to deal with imprecise and uncertain reservoir information. Due to its success in Kern River, the tech...

Intelligent control of CO₂-EOR process

“…In the case of fuzzy logic which enables a formal record of imprecise natural language concepts there are a number of intermediate values determining the membership degree between the lack of membership (absolute false) and full membership (absolute true) [12]. Fuzzy logic has made it possible to realistically assess the location of new wells on the reservoir thanks to the extended approach to the description of reservoir rock parameters [10]. However, fuzzy logic is most often used in combination with other artificial intelligence methods.…”

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

“…Infill drilling helps in the recovery of bypassed reserves and could be optimized if the inherent uncertainties in reservoir properties/geology are incorporated using fuzzy logic. Popa (2013) presented a case where they applied fuzzy logic model to horizontal well identification using a complex 3D full field earth model developed with data from more than 21,000 wells. He reported that the application of fuzzy logic for horizontal well identification maximized production outcome of the field.…”

“…Elements of sets with degrees of membership functions are generally known as fuzzy sets. A membership function allows for the fuzzification of both input and output variables (Popa 2013). With fuzzy rules generated and alpha-cuts taken from membership function, sensitivity analysis of input values is carried out to generate a robust output response.…”

Optimization of Kelanting’s Production in a Home Industry Kelanting Fuzzy System

“…In helping prediction, one alternative is by using soft computing techniques such as neural networks and fuzzy logic [4]. Fuzzy logic is an approach that deals with uncertain information and some aggregate variables with the appropriate membership function [5]. Fuzzy logic is a scientific method [6].…”