What are the most effective ways to analyze well logs for formation evaluation?

Petrophysics: Petrophysical properties such as shale volume, water saturation, permeability, etc. are more geology-like and can be used to characterize reservoirs. These properties can be calculated using well log data. Rock physics: Rock physics properties such as elasticity, wave velocity, etc. are more geophysics-like and can also be used to characterize reservoirs. These properties can also be calculated using well log data. RHOB-NPHI cross over: This method is used to find a hydrocarbon-bearing zone. Reflectivity coefficient: This method is used to find a hydrocarbon-bearing zone, just like in seismic interpretation. AI anomaly: This method is used to find a hydrocarbon-bearing zone

In my opinion, the first step in well log analysis is to discover the investigation goals of the end consumer of the investigation analyses. There may be a long list of goals with a subset of goals that are practical in an immediate business circumstance. The accepted subset list of goals will pretty much determine what logs will be run, which logging suite will also pretty much focus well log analyses processes and practices.

When we are doing well log analysis we need to look at all the well logs together. Calliper may be the most important because it identifies washout zones and tight formations. Many will look at the gamma-ray for the lithology. Then we have resistivity but we should overlay all three to look for invasion. When we’ll fluids are invading the rock it indicates that it is porous/permeable. We need to look at the neutron and density together to understand if we have hydrocarbons in the well. We also need to look at the sonic. With sonic there will be a decrease in the velocity or an increase in the interval transit time (1/velocity *10^6). By doing a full interpretation of all the wells we begin to understand what is happening in the formation.

Crucial to well-log analysis is a profound understanding of logging tool functions. These tools measure properties like resistivity, sonic travel times, and gamma radiation, providing key insights into rock and fluid characteristics. This knowledge is foundational, enabling analysts to strategically combine measurements for enhanced reservoir characterization. Adept interpretation of logging principles is indispensable, ensuring accurate calculations of parameters such as net pay and porosity. In essence, the importance lies in leveraging this understanding to unlock the full potential of well logs, offering a precise and comprehensive assessment of reservoir quality and potential.

Archie's equation is helpful in calculating hydrocarbon saturations in clastic reservoirs. The quicklook log interpretation helps one to suggest points for the formation pressure tool. It is required to run the pressure/sampling tool to acquire pretests and possibly downhole samples. These data are also used by the reservoir engineer, production technologist, and petrophysicist in determining the fluids present in the formation. Using the formation pressure data points acquired, one is able to calculate the formation-fluid densities and position of free water level

Log quality control, including log conditioning, is instrumental not only for traditional interpretation but also enhances reliability in AI and ML applications. Clean and refined well log data, free from anomalies, provides a more solid foundation for machine learning algorithms. This ensures that AI models generate more accurate and trustworthy analyses, contributing to a more robust and reliable automated interpretation of subsurface formations. The integration of log quality control becomes imperative for optimizing the effectiveness of advanced technologies in well log analysis.

Because seismic-based reservoir characterization is the pivotal way to extend anomalies over a large area of exploration, the validation of those anomalies requires synthetic modeling first to identify the real interpretation of each anomaly (oil, gas or others). The acquired seismic contrast (reflection coefficient) is quite tiny by nature (Ex. The maximum RC after accounting for all losses is around 0.3 for some regions). As a result, a lithology change estimated by a certain %RC will be difficult to characterize, and data QC prior to modeling will be critical. For these reasons, as well as the serious consequences associated with the final obtained results, experts consider data QC and preconditioning as about 50% of the overall project.

Always review the source data, tool, and company and compare different memory and LWD data. the essential thing is Documenting Logging keek an eye on Procedures: A good check list and proper documentation of logging procedures, including tool specifications, operational parameters, and any deviations from standard procedures, is essential for quality control and for tracing the source of any data discrepancies.

AI and ML techniques revolutionize log interpretation by enhancing accuracy and efficiency. These technologies excel at discerning complex patterns within well log data, offering precise insights into reservoir properties like lithologies and fluid saturations. Machine learning models, adaptable and continually improving, excel in handling the intricacies of subsurface formations. Real-time analysis and task automation further optimize well log interpretation, allowing for swift decision-making and freeing human resources for more strategic aspects of reservoir characterization.

In well log analysis, the use of open-source languages like Python and R is crucial for several reasons. Commercial software may have limitations, but these languages offer flexibility, allowing customization and adaptation to novel methods like Fractal and ML analysis. Open-source platforms empower analysts to tailor solutions to specific challenges, fostering innovation and expanding the toolkit for reservoir characterization. Additionally, the collaborative nature of these languages facilitates knowledge-sharing and the rapid adoption of cutting-edge techniques, ensuring that the industry stays at the forefront of advancements in well log analysis.

Analyzing well logs for formation evaluation effectively involves a combination of technical approaches and expertise in geology and petrophysics.

In advancing well log analysis, incorporating preprocessing steps like data normalization and outliers detection and removal is crucial. Normalizing log data ensures comparability, while identifying and removing outliers enhances data reliability. These preprocessing techniques, when combined with multifractal and lacunarity analyses, and ML/AI approaches, create a comprehensive framework. They address challenges like incomplete data and unconventional reservoir complexities, fostering robustness for accurate and meaningful insights in well log analysis.

Absolutely, adopting standards based on practical experiences from various experts can establish a valuable reference for best practices in log analysis. Drawing on the collective wisdom of seasoned professionals provides a comprehensive and nuanced approach. These standards can encompass defining clear objectives, utilizing diverse data sources, applying consistent models, documenting assumptions, and ensuring robust verification processes. By amalgamating practical experiences into standardized best practices, the industry can cultivate a shared framework that enhances the reliability and effectiveness of well log analysis across different scenarios and challenges.

Creating referential standards based on the practical experiences of diverse experts and integrating a multitude of analytical methods lays a robust foundation for well log analysis. A comprehensive database, encompassing a wide range of scenarios and insights, facilitates the extraction of meaningful knowledge. This wealth of information not only aids in current analyses but also serves as a valuable resource for continuous learning and improvement. Establishing such a repository enhances the industry's collective understanding and proficiency in well log analysis, fostering a dynamic and adaptive approach to address evolving challenges.

If available, make use of geophysical surveys to cross-check if your log analysis is compatible with the velocity model. Usually the well logs are used to help calibrate the geophysical velocity model.

Sometimes working with old data or data from other companies is a huge challenge. On one occasion, the information had to be validated with a file that was no longer valid, so you always have to update the data.