What are the current challenges and opportunities for formation evaluation in unconventional resources?

1 Logging challenges

One of the main challenges in formation evaluation in unconventional resources is obtaining reliable and representative measurements of the reservoir properties, such as porosity, permeability, saturation, mineralogy, organic matter, and fracture network. Conventional logging tools, such as wireline and logging while drilling (LWD), may not be adequate or feasible for these low-porosity, low-permeability, and highly laminated formations. Moreover, some unconventional wells may have limited or no access for logging, due to horizontal or multilateral drilling, or operational and economic constraints. Therefore, there is a need for developing and deploying new logging tools and techniques that can overcome these limitations and provide more accurate and comprehensive data.

2 Logging opportunities

Despite the challenges, formation evaluation in unconventional resources presents opportunities for innovation and improvement in logging technology and methodology. For example, microseismic logging uses passive seismic sensors to detect and locate micro-earthquakes induced by hydraulic fracturing and map the fracture network. Nuclear magnetic resonance (NMR) logging leverages the magnetic properties of hydrogen atoms to measure porosity, permeability, fluid type, and fluid distribution in the pore space. Spectroscopy logging utilizes the interaction of electromagnetic radiation with matter to measure elemental and mineralogical composition of the formation, as well as estimate total organic carbon (TOC) content. Lastly, dielectric logging utilizes electric properties of matter to measure water saturation and salinity, as well as differentiate between bound and free water in the pore space.

3 Data integration

Another challenge in formation evaluation in unconventional resources is integrating and interpreting the large and diverse data sets obtained from various logging tools, core samples, well tests, and production data. The integration and interpretation of these data sets require advanced software and algorithms that can handle the complexity and uncertainty of the unconventional reservoirs, and provide meaningful and consistent results. Moreover, the data integration and interpretation should be done in a multidisciplinary and collaborative manner, involving geologists, petrophysicists, geophysicists, reservoir engineers, and production engineers.

4 Data integration opportunities

The challenge of data integration also presents opportunities for developing and applying new data analysis and visualization techniques to enhance the understanding and evaluation of unconventional reservoirs. Machine learning, digital rock physics, data analytics, and data visualization are some of the emerging data integration techniques. Machine learning employs artificial intelligence and statistical methods to learn from data and make predictions or decisions without explicit programming or rules. Digital rock physics utilizes high-resolution images of rock samples to create numerical models of the pore structure and fluid flow, and to simulate the physical measurements of logging tools. Data analytics involves mathematical and statistical methods to analyze large and complex data sets, while data visualization employs graphical and interactive tools to display and explore data sets, as well as communicate results and findings.

5 Interpretation challenges

The final challenge in formation evaluation in unconventional resources is translating the data and information obtained from logging tools and data integration into meaningful and actionable reservoir parameters and properties, such as hydrocarbon volume, recovery factor, well performance, and reservoir quality. The interpretation of these parameters and properties requires a thorough understanding of the geological, petrophysical, geophysical, and engineering aspects of the unconventional reservoirs, as well as the uncertainties and risks involved. Moreover, the interpretation should be updated and refined as more data and information become available during the life cycle of the reservoir.

6 Interpretation opportunities

Interpreting unconventional resources presents a challenge, but also offers opportunities for developing and applying new interpretation methods and models that can improve formation evaluation accuracy and reliability. Geomechanical modeling, for instance, uses the mechanical properties of rock and fluid to simulate reservoir stress and strain behavior, as well as fracture initiation, propagation, and closure. Petrophysical modeling leverages physical and chemical properties of rock and fluid to simulate electrical, acoustic, nuclear, and magnetic measurements of logging tools. Reservoir modeling uses the spatial distribution of reservoir parameters to create a three-dimensional representation of the reservoir and simulate fluid flow and transport processes. Finally, production modeling uses the reservoir model and well data to simulate production performance and decline of wells, as well as optimize completion and stimulation design.

7 Here’s what else to consider

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