Well performance: Rock properties using existing field data & real-time drilling data

February 2018January 2020
Insufficient amounts of sample material affects the quality or calibration of the well log information, resulting in high data uncertainty.

Well performance: Rock properties using existing field data & real-time drilling data

The main aim of this project is to determine the rock properties by integrating existing field data and real-time drilling data. The project will explore better ways to improve the drilling data acquisition and to correlate rock properties with well logs to gain greater data certainty.

Calibration of well logs will be conducted to improve the certainty of the logging data, which is needed for the prediction of reservoir stimulation and understanding of reservoir performance.

BACKGROUND

Rock properties and stress profiles are critical for understanding wellbore stability, fracture propagation and reservoirs dynamic response to dewatering and the subsequent gas extraction. However, sufficient amounts of sample material are commonly not available due to the inherence low core retrieval, as well as to high drilling and sample costs. This affects the quality or calibration of the well log information, resulting in high data uncertainty.

PROJECT OUTPUTS

Expected outputs

  • Developing an optimal drilling data acquisition system for rocks using high speed data
  • Developing a system to determine rock properties from realtime data
  • Developing a function to calibrate well logging data to improve data certainty
  • Developing a systematic approach to implement technologies to better steer and locate wells in optimal intervals by completion style

 

  • Project status: Current
  • Project title: Development of rock properties using existing field data and real-time drilling data
  • Project leader: Ray Johnson
  • Project team: Dr Zhongwei Chen
  • Research group: The University of Queensland School of Chemical Engineering & The University of Queensland Centre for Coal Seam Gas
  • Timeframe: February 2018 - January 2020
  • Project funders: APLNG, Arrow Energy, Santos, University of Queensland