How do you optimize well placement and completion based on porosity and permeability distribution?

1 Data sources and integration

The first step is to gather and integrate all the available data sources that can provide information on porosity and permeability distribution. These may include core, log, seismic, production, and well test data. Each data source has its own advantages and limitations, and they need to be calibrated and validated with each other. For example, core data can provide direct measurements of porosity and permeability, but they are limited in number and may not represent the whole reservoir. Log data can provide continuous profiles of porosity and permeability, but they are affected by borehole conditions and require interpretation models. Seismic data can provide 3D images of the reservoir, but they have low resolution and need to be inverted to obtain petrophysical parameters. Production and well test data can reflect the dynamic behavior of the reservoir, but they are influenced by wellbore and surface conditions and require history matching.

2 Geostatistical modeling

The second step is to use geostatistical modeling to generate realistic and consistent 3D models of porosity and permeability distribution. Geostatistics is a branch of statistics that deals with spatial data and incorporates spatial correlation and variability. Geostatistical modeling involves several steps, such as defining the geological framework, selecting the appropriate variogram models, choosing the interpolation or simulation methods, and performing quality control and uncertainty analysis. Some of the common methods used in geostatistical modeling are kriging, co-kriging, sequential Gaussian simulation, sequential indicator simulation, and multiple-point statistics.

3 Well placement optimization

The third step is to use the geostatistical models to optimize the well placement. Well placement optimization is the process of finding the best location, orientation, and trajectory of a well to maximize the production performance and economic value. Well placement optimization can be done using various criteria, such as net present value, recovery factor, drainage area, productivity index, or reservoir contact. Well placement optimization can also be done using different techniques, such as analytical methods, numerical methods, proxy models, or evolutionary algorithms.

4 Completion design optimization

The fourth step is to use the geostatistical models to optimize the completion design. Completion design optimization is the process of finding the best configuration of the wellbore and the completion equipment to enhance the flow of fluids and the control of the reservoir. Completion design optimization can involve various aspects, such as perforation, stimulation, artificial lift, sand control, inflow control devices, or intelligent completions. Completion design optimization can also be done using different criteria, such as skin factor, injectivity index, pressure drop, or flow rate.

5 Sensitivity analysis and validation

The fifth step is to perform sensitivity analysis and validation of the optimized well placement and completion design. Sensitivity analysis is the process of evaluating how the results change with respect to changes in the input parameters or assumptions. Sensitivity analysis can help identify the key factors that affect the performance and the uncertainty of the optimization. Validation is the process of comparing the results with the actual or expected outcomes. Validation can help verify the accuracy and reliability of the optimization and identify the possible errors or discrepancies.

6 Continuous improvement and learning

The sixth step is to implement continuous improvement and learning based on the feedback and experience from the optimized well placement and completion design. Continuous improvement and learning is the process of updating and refining the data, models, methods, and criteria based on the new information and knowledge gained from the optimization. Continuous improvement and learning can help enhance the understanding of the reservoir behavior, improve the quality of the optimization, and increase the efficiency and effectiveness of the petrophysical workflow.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?