Embedded Discrete Fracture Modeling With Artificial Intelligence in Permian Basin
- Chris Carpenter (JPT Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- May 2018
- Document Type
- Journal Paper
- 63 - 64
- 2017. Society of Petroleum Engineers
- 2 in the last 30 days
- 126 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||Free|
|SPE Non-Member Price:||USD 17.00|
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 187202, “Field Study: Embedded Discrete Fracture Modeling With Artificial Intelligence in Permian Basin for Shale Formation,” by Song Du, Baosheng Liang, and Lin Yuanbo, Chevron, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 9–11 October. The paper has not been peer reviewed.
Full-field models using unstructured grids can capture detailed geometric information such as fracture distribution. However, these are computationally expensive and often numerically unstable because of convergence issues. In the complete paper, the authors investigated embedded discrete fracture modeling (EDFM) using artificial intelligence (AI) to overcome challenges associated with unstructured modeling.
It has been proved that EDFM enables flexible fracture geometry because the fracture domain is relatively independent of the matrix regions. EDFM has been widely accepted recently because of its simplicity and computational efficiency. The authors’ studies applied EDFM with AI optimization for fracture-network representation.
The growth of computational power and the availability of large quantities of data have led to the widespread promotion of applications of AI technologies. AI has been used in the oil and gas industry in the areas of production optimization, operating-cost reduction, and efficiency improvement. Currently, almost all AI technologies are still limited to executing specific tasks, also known as specialized AI, an application that lacks the capability of generalized AI in adaptive learning. However, specialized AI still has advantages of high speed, superior consistency and unmatchable repeatability over human intelligence. In the complete paper, application of AI technology in processing the fracture network for successive simulation is described.
EDFM. This method has drawn considerable attention because of its efficiency. EDFM minimizes the local grid resolution while preserving flow behavior between fracture and matrix by maintaining the original fracture orientation and distribution. The EDFM method is composed of two major elements: matrix and fracture. A long fracture is subdivided into small segments at the interconnecting points. A simple material-balance equation can show the flow behaviors at the cross sections.
The fractures and matrix are modeled as two different domains. As de-scribed previously, fluid communication between fractures and the matrix uses mass-balance equations. Nonneighbor connection (NNC) must be calculated explicitly for a reservoir simulator.EDFM With Mangrove. Mangrove, an engineered stimulation design package, is a hydraulic-fracture simulator that links reservoir characterization and simulation and helps optimize completion designs in unconventional reservoirs. It has been of considerable use in unconventional-field development and has provided guidance for well-landing, completion-design, and well-/fracture-optimization decisions. However, the package creates unstructured simulation grids that can lead to convergence issues, and is also computationally expensive.
|File Size||464 KB||Number of Pages||2|