How do you use petrophysics to optimize well placement in tight oil reservoirs?

1 Petrophysical data sources

The main sources of petrophysical data are well logs, core samples, and fluid samples. Well logs are measurements of various parameters along the wellbore, such as resistivity, porosity, density, and gamma ray. Core samples are cylindrical pieces of rock extracted from the wellbore, which can be analyzed in the laboratory for more detailed properties, such as mineralogy, saturation, and rock mechanics. Fluid samples are collected from the formation or the wellbore, and can provide information on the composition, viscosity, and pressure of the oil and gas.

2 Petrophysical analysis methods

Petrophysical analysis methods vary depending on the type and quality of data available, as well as the objectives of well placement. Common methods include net pay determination, which is used to identify intervals with producible hydrocarbons, geostatistical modeling to create a three-dimensional representation of reservoir properties, geomechanical modeling to simulate rock behavior and wellbore stability, and hydraulic fracture modeling to predict fracture geometry and conductivity. All these methods are important for enhancing production.

3 Well placement optimization criteria

The well placement optimization criteria depend on a variety of factors, such as reservoir characteristics, drilling constraints, and economic factors. When considering these criteria, it is important to measure reservoir contact for its effect on the drainage area and recovery factor, fracture stimulation for its effect on productivity and decline rate, well interference for its effect on pressure depletion and recovery efficiency, and drilling risk for its effect on wellbore quality and operational safety.

4 Petrophysical uncertainty quantification

The petrophysical uncertainty quantification is the process of assessing and reducing the uncertainty in the petrophysical data and analysis, and how it affects the well placement optimization. Sources of uncertainty may include data quality, data interpretation, and data integration. Data quality measures how accurate and representative the data are, and how they are affected by measurement errors, sampling biases, and environmental factors. Data interpretation measures how reliable and consistent the data are, and how they are affected by assumptions, models, and parameters. Data integration assesses how coherent and compatible the data are, and how they are affected by scaling, averaging, and correlation.

5 Petrophysical best practices

The petrophysical best practices are the guidelines and recommendations for using petrophysics to optimize well placement in tight oil reservoirs, based on the experience and knowledge of the experts and the industry. It is important to use multiple data sources and methods to cross-check and validate the results, and to capture the heterogeneity and variability of the reservoir. Probabilistic and scenario-based approaches should be used to account for uncertainty and risk, as well as to evaluate sensitivity and robustness. Multidisciplinary and collaborative teams should be employed to integrate petrophysical data and analysis with other disciplines, such as geology, geophysics, reservoir engineering, and drilling engineering. Finally, continuous learning and feedback loops should be utilized to update and improve petrophysical data and analysis, while adjusting well placement optimization accordingly.