The exploration and development of unconventional oil and gas resources have become anew hot frontier in China. In these reservoirs, the porosity is mainly less than 10%, and the permeability is mainly less than1.0mD. Hence, to maximize the productivity, horizontal wells are drilled, and big scale sand fracturing is usually designed to solve the problem. Horizontal well drilling is extremely difficult due to high heterogeneity and uncertain fault and fractures. The reservoir quality and completion quality vary along the borehole, and as a result, itis very difficult to optimize the stimulation design to unlock the potential of the reservoir.
To provide better understanding of the reservoir in horizontal wells, we introduced an integrated solution which incorporates the reservoir evaluation for the pilot well and horizontal well and provided inputs for the optimization of horizontal well drilling and stimulation. Firstly, in the pilot well, we integrated micro-resistivity logging data, gamma-ray spectroscopy logging data, 2D NMR data to get accurate lithology, TOC, porosity, and oil saturation with an unconventional evaluation approach. Then, we built the petrophysical evaluation models using some AI algorithm, and criteria for horizontal wells. And the geologist conducted an accurate well correlation and built a3D structural model with new technology based on resistivity image data and conventional logs to target the best reservoir as much as possible when drilling the horizontal well. Finally, we combined Reservoir Quality (RQ), Completion Quality (CQ), and 3D structural models to design depths for the stages and perforation clusters and helped to optimize the stimulation design and helped to enhance the oil production.
With this approach, we integrate the RQ and CQ parameters into the 3D structural model, not only can we evaluate the effective porosity, saturation, minerals, fractures and TOC along the wellbore, but also can get a clear picture of these properties in 3D distribution. Hence, multistage hydraulic fracturing design can be made more effectively and at lower cost. Application of this approach in several wells has helped to increase the production by 20%.
It was used to use mud logs to divide the stages for fracking, and as a result, we could not get a clear idea about the RQ and CQ along the wellbore and close to the wellbore. We introduced movable oil porosity as a key petrophysical parameter to rank the reservoir more accurately and build a better geological model close to the wellbore to provide the guidance for fracking design.
