What are the different ways to interpret well logs for petrophysical analysis?

Interpreting well logs involves visual inspection, quantitative analysis, and cross-plotting to identify lithology and fluid content trends, aiding in formation evaluation and decision-making in the oil and gas industry.

Most basic of all, any petroleum engineer should atleast know how to interpret based on the basic logs and plot them. Various parameters like porosity, saturations, crossovers, geological deposition, type of formation etc could be the very starting point of your interpretation.

Well logs analysis provide detailed information about subsurface formations. They include measurements of electrical resistivity, natural gamma radiation, acoustic properties, and others. These measurements allow geologist to evaluate lithology, identify hydrocarbon-bearing zones, estimate porosity and permeability, as well as calibrate seismic data. Well logs analysis are one of the inputs for reservoir characterization to build accurate geological models, reservoir development and management. Understanding these essential data types is vital for successful characterization efforts.

In the oil and gas industry, essential parameters include gamma ray, resistivity, porosity, and acoustic properties for formation evaluation. Additionally, monitoring fluid flow rates, pressure, temperature, and chemical composition aids in optimizing production and reservoir management. Gamma ray measurements correlate lithology, while resistivity indicates hydrocarbon presence and fluid saturation. Porosity assessments determine reservoir capacity, while acoustic properties help identify potential production zones. Fluid flow rates and pressure data optimize production strategies, while temperature monitoring provides insights into fluid behavior and reservoir properties. Analyzing chemical composition ensures efficient production.

Well-logs provide various information about geological layers, including their boundary depths. The manual well-to-well correlation is usually implemented to identify lithological boundaries and to understand the connectivity of hydrocarbon layers and construct 3-D static reservoir models Thus, the automated well to well correlation methods have received more attention as they might reduce interpretation costs.

Log correlation is the process of aligning and comparing different log measurements from various depths within a wellbore to create a coherent understanding of subsurface geology. By correlating logs such as gamma ray, resistivity, and porosity, geoscientists can identify lithological boundaries, stratigraphic layers, and hydrocarbon-bearing formations. This integration enhances reservoir characterization, wellbore placement, and drilling decisions, optimizing hydrocarbon recovery. Log correlation enables the construction of detailed geological models, facilitating accurate reservoir mapping and resource estimation.

A few examples of log modeling: 1) Log reconstruction is employed in probabilistic petrophysical interpretation. This involves simulating the reconstruction of input data logs (such as GR, Resistivity, Neutron, Density, etc.) by defining input parameters ( mineral content, matrix density, salinity, temperature, saturation parameters, etc). The primary goal of probabilistic interpretation is to ascertain petrophysical properties (such as Vclay, Porosity, saturation, etc.) while simultaneously reconstructing the input log. 2) Fluid substitution is utilized in log forward modeling to predict log responses, particularly in elastic logs' response to fluid content. There are more examples of log modeling that can be explored.

To attain a comprehensive understanding of reservoir character, integrating data from various scales is crucial. Experience in merging log data with core analysis, PVT analysis, production records, and seismic data enhances the robustness of reservoir properties in modeling. During rock typing and facies determination, a meticulous calibration of log-derived rock typing with sedimentology studies from core data and deterministic rock typing from SCAL data is conducted. This method categorizes rock types into facies classes, providing a thorough understanding of reservoir rock quality. Furthermore, cross-validating rock typing with elastic logs for integration into seismic evaluation optimizes both static and dynamic reservoir modeling.

Ultimately log integration help to integrate them in the models along with the various parameters that has to be interpreted. It’s essential to match the logs along with the NTG of the static model to make representative dynamic simulations.