Abstract
In oil drilling, mud filtrate penetrates into porous formations and alters the compositions and properties of the pore fluids. This disturbs the logging signals and brings errors to reservoir evaluation. Drilling and logging engineers therefore deem mud invasion as undesired, and attempt to eliminate its impacts. However, the mud-contaminated formation carries valuable information, notably with regard to its key hydraulic properties. Typically, the invasion depth critically depends on the formation porosity and permeability. Therefore, if adequately characterized, mud invasion effects could be utilized for reservoir evaluation. To pursue this objective, we apply borehole radar to measure mud invasion depth considering its high radial spatial resolution compared with conventional logging tools, which then allows us to estimate the reservoir permeability based on the acquired invasion depth. We investigate the feasibility of this strategy numerically through coupled electromagnetic and fluid modeling in an oil-bearing layer drilled using freshwater based mud. Time-lapse logging is simulated to extract the signals reflected from the invasion front, and a dual-offset downhole antenna mode enables time-to-depth conversion to determine the invasion depth. Based on drilling, coring, and logging data, a quantitative interpretation chart is established, mapping porosity, permeability, and initial water saturation into invasion depth. The estimated permeability is in a good agreement with the actual formation permeability. Results therefore suggest that borehole radar has significant potential to estimate permeability through mud invasion effects.
| Original language | English |
|---|---|
| Pages (from-to) | H51-H60 |
| Number of pages | 10 |
| Journal | Geophysics |
| Volume | 85 |
| Issue number | 4 |
| Early online date | 10 Jun 2020 |
| DOIs | |
| Publication status | Published - 1 Jul 2020 |
Bibliographical note
We would like to express our gratitude to C. Warren at Northumbria University for the valuable help in gprMax modeling and W. Filinger at The University of Edinburgh and J. Liu at the Delft University of Technology for their assistance in the high-performance computing. We acknowledge the Sinopec Petroleum E&P Institute for the permission to use the oil field logging and coring data. The research was funded by the National Natural Science Foundation of China (41674138, 41811530749, 41974165), the NWO Cooperation and Exchange Fund (040.22.011/7048), and the China Scholarship Council grant (201806415048). The work has been performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 program, and used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).DATA AND MATERIALS AVAILABILITY
Data associated with this research are available and can be obtained by contacting the corresponding author.