Maximizing Image Data To Minimize the Uncertainty of the Geological Model for Horizontal Well Fracturing

Abstract

Abstract

Multistage fracture technology for horizontal wells has opened up development of tight sand reservoirs worldwide. Accurate sand distribution models are the key to this technology. They can be used to optimize the fracturing parameter design for each stage based on the 3D heterogeneous sand characterization.

The geology modeling workflow introduced in this paper is much different from previous approaches. The image data is maximized in this method to minimize the uncertainty of the geology model.

Image data can characterize the subsurface structure and sedimentary structure accurately. The horizontal bedding of the shale picked from the image is used to generate the near-well surface using a novel modeling plug-in in an E&P software platform[SP1]. This surface reflects the real structure variation near the well. The 3D structure model uncertainty will be minimized by the integration of the near-well surface.

The sand model is built using the sequential indicator simulation method based on the lithology from the borehole. Sand geometry can be expressed by variogram parameters. So reliable input parameters from the variogram will greatly improve the accuracy of the sand model. Multiwell paleocurrent analysis from cross bedding picked from the image will predict the source direction precisely. Sedimentary structure analysis and sand description form the image data will help to set anisotropy ranges of major, minor, and vertical directions more reasonably.

For each stage of fracturing, the parameter will be optimized separately based on the sand distribution near the horizontal well. Seven horizontal wells fractured with the sand model have had better productivity performance. About 30% more production of single well is enhanced compared with previous wells without this sand model. This workflow optimizes tight hydrocarbon reservoir fracturing design and can be extended to other tight hydrocarbon reservoirs around the world where the image data is available.

AAPG Datapages/Search and Discovery Article #90260 © 2016 AAPG/SEG International Conference & Exhibition, Cancun, Mexico, September 6-9, 2016