A feature-based well completion optimization system applying fuzzy MCDM

  • Author / Creator
  • Well completion is an intermediate process for oil production, or simply a connection between well drilling and oil recovery, it can influence wells’ productivity, stability and longevity directly, hence has important effect on economic benefit of oilfield development. The selection of well completion method involves a wide range of knowledge, and multiple perspectives to be considered. However, almost all the commonly adopted well completion selection models and methods are not comprehensive. Some of them depend only on experts’ knowledge, experience and judgement, which are too subjective and cannot cover all the aspects needed to be considered during the selection. Others only depend on numerical indexes, like initial production, operation cost etc., which ignore experts’ knowledge and are overly simplified. Therefore, this thesis is intended to build an optimum well completion method selection system to provide a solution for this multi-objective problem. Research works included in this thesis are briefly described as follows. • Establishing rules for well completion method selection. This thesis summarizes these commonly used well completion methods for both vertical and horizontal wells, and their applicable conditions, advantages and disadvantages are analyzed to help proposing selection rules of well completion methods. • Building an evaluation prototype system. Five primary evaluation aspects are included: reservoir failure mode identification, stimulation technology determination, productivity, cost and HSE. Each of these criteria has its own corresponding second level indexes, and both qualitative and quantitative indexes are included in this system. • Proposing a new object data structure to support the software development feature. Well completion method selection feature is proposed, this feature not only can integrate different expert areas together to support the complex decision-making process, but also can reduce rework and iterations. In addition, using the feature modelling approach can bring all independent modules together to describe the full system in a coordinated and comprehensive way. Therefore, with this newly defined feature as a generic solution mechanism, this system can be expanded easily to include new well completion methods and evaluation indexes. • Building a new weight determination scheme for well completion selection. The new scheme not only combines subjective and objective method together, but also integrates MODM and MADM into one system. • Applying the proposed selection system on two different cases to demonstrate the feasibility of this system and developing a well completion methods optimum selection software prototype with C# language.

  • Subjects / Keywords
  • Graduation date
    Spring 2018
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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