Machine Learning
Machine Learning
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Reservoir characterization:
combining machine intelligence
with human intelligence
An integrated approach for reservoir characterization bridges the traditional disciplinary divides, leading to better handling of uncertainties and improvement of the reservoir model for field development.
Fred Aminzadeh, University of Houston

eservoir characterization is the process of assessing reservoir properties and its condition, using the available data from different sources such as core samples, log data, seismic surveys (3D and 4D) and production data. This is done in different stages of the E&P process from high grading reservoirs in exploration to their delineation for their development, as well as their description for optimum production to assessing their evolution in their stimulation for enhanced oil/gas recovery to extend their economic life.

An integrated approach for reservoir characterization bridges the traditional disciplinary divides, leading to better handling of uncertainties and improvement of the reservoir model for field development.

Among the main difficulties in reservoir characterization is the “SURE challenge.” Figure 1 demonstrates the complications involved in integrating different data types with different scale, uncertainty, resolution and environment (SURE). The top left illustrates three key data types: core, well log and seismic data (referred to as a data pyramid). The base of the pyramid is the seismic with very large coverage but with limited resolution and lesser level of certainty. The top of the pyramid is the core data with very little coverage (only at a particular well location involving a fraction of the well) but with high level of certainty and resolution.

Effective integration of all the data types, in spite of the SURE challenge, is the objective of reservoir characterization. Artificial intelligence (AI) and data analytics (DA) can play key roles in offering solutions to the SURE challenge.

AI-DA has been gaining popularity in many aspects of E&P recently, and the expectation is that it will become an integral part of the tool box for many of our applications. DA, which is the systematic use of computational analysis of the data for making decisions, is an appropriate tool to address the need to deal with large amounts of data (Figure 1). The DA engine is energized by the power of AI and its machine learning (ML) and deep learning (DL) subsets. AI-DA may prove to be the exact medicine to address the SURE challenge.

The SURE challenge highlights the difficulties in integrating data with wide ranging differences in terms of scale, uncertainty, resolution and environment
FIGURE 1. The SURE challenge highlights the difficulties in integrating data with wide ranging differences in terms of scale, uncertainty, resolution and environment. (Source: AIM-DEEP/University of Houston)
The bottom right of Figure 1 shows a pyramid comprising different aspects of integration. Vast amounts of Big Data with their 4V characteristics (volume, velocity, variety and veracity) need to be combined with technical knowledge and experience from domain experts to perform effective data mining and ultimately reservoir characterization. This requires designing a human machine interface, perhaps based on fuzzy logic and natural language processing to facilitate flow of data and information between the two.

An integrated reservoir characterization starts with collecting data from geological, petrophysical, seismic and engineering data. A multidisciplinary data analysis process creates a model of different reservoir properties including reservoir architecture, lithologies and facies. The geometry of the flow units is established (physical rock properties such as porosities and permeabilities of flow units). Three properties are related to the pore space:

  • Porosity: the fraction of the entire volume part occupied by pores, cracks and fractures;
  • Internal surface: the magnitude of the surface of pores as related to the rock mass pore volume and controls interface—effects at the boundary grain—pore fluid; and
  • Permeability: the ability to flow fluid through rock pores.
Graphic of hierarchy of data analytics, artificial intelligence, machine learning and deep learning, all of which provide a new set of tools for E&P
FIGURE 2. The graphic depicts the hierarchy of data analytics, artificial intelligence, machine learning and deep learning, all of which provide a new set of tools for E&P. (Source: AIM-DEEP/University of Houston)
Given different levels of uncertainty and other aspects of the SURE challenge, the estimates of reservoir properties should also be accompanied with their respective levels of uncertainty. This is derived from the calibration process and the extent of the match between estimated models with the ground truth (well/production data). This necessitates integration of physics-based and data-based approaches, also referred to as hybrid methods. Reservoir description is an iterative process from the input data to the process (e.g., well data, seismic data and production data). High-performance computers, both for their computing power and memory capacity, are crucial for performing data mining and iterations in a timely fashion, especially for real-time reservoir monitoring.

AI-DA offers a natural toolbox for reservoir property estimation and their uncertainties. ML and DL methods perform much like a human brain. They can receive a variety of data from many different sources with drastically different characteristics and undertake necessary evaluations, and they can eventually make the right decisions and/or solve complicated problems. For example, DL finds particular features in the data that could be useful for classification of facies or prediction of different reservoir properties (Figure 2). They are well equipped to handle the issues highlighted under the SURE challenge.

Nevertheless, human intelligence (engineers and geoscientists) will always have a superior performance with qualitative data than computers that are better dealing with quantitative data. Thus, we should design effective human-machine interfaces to create hybrid solutions based on combining machine intelligence with human intelligence.
Program bridges the gap between O&G and academia
University’s program focuses on AI and data analytics technologies for energy E&P.
Ariana Hurtado, Senior Managing Editor, Publications

niversity of Houston’s (UH) new AIM-DEEP program, which stands for artificial intelligence (AI), machine learning and data analytics for energy exploration and production, was launched to fill the gap between the ever-increasing advances in AI and data analytics and the growing demand for such technologies in various oil and gas and other energy-related applications.

“AIM-DEEP is expected to emerge as a unique platform to help speed up infusion of AI, machine learning and data analytics concepts into the energy exploration and production arena,” said Fred Aminzadeh, professor and AIM-DEEP director with the University of Houston.

“AIM-DEEP will do this by bringing oil and gas operators, service companies, high-tech computer/data companies and academia together,” he continued. “We want to benefit from various ongoing research on AI, not only in different departments at UH but also those of many other academic and research partners of AIM-DEEP. It will serve as a catalyst to break down the discipline, organization and industry versus academia boundaries to keep pace with the fast-evolving AI/data analytics technologies.”

Aminzadeh recently detailed the university’s AIM-DEEP program in a written interview with E&P Plus.

E&P Plus: What are some of the program’s focus areas in the E&P sector of the oil and gas industry?

Aminzadeh: The program is focused on effective use of AI in different aspects of E&P. The top five focus areas based on the votes from the current and prospective sponsors are:

  • Value addition of high-performance computing and AI for oil and gas applications;
  • Intelligent seismic attribute analysis and reservoir characterization;
  • Machine learning/AI/data analytics for production cost reduction of unconventional resources;
  • Integrating physics-based and statistics-based approaches using AI and data analytics; and
  • Digitalization: Getting the most value out of digital threads and digital twins in oil and gas.
Fred Aminzadeh headshot
Fred Aminzadeh
E&P Plus: How is this program a catalyst for transformative changes in the areas of reservoir characterization and evaluation, 3D seismic and/or subsurface imaging?
Aminzadeh: We expect to be a change agent in the way subsurface imaging and reservoir analysis is done. This will combine the power of AI and data analytics with the many advances in high-performance computing and memory (both physical and cloud/edge-based) to carry out modeling, imaging and simulation tasks more efficiently and faster in a cost-effective manner. We will help unleash the strength of AI and data analytics to address various Big Data challenges of the energy industry. The transformative changes resulting from more widespread use of AI and data analytics will be of the scale of the role horizonal drilling and hydraulic fracturing have played in the development of shale resources had in the last 20 years.
E&P Plus: What are the benefits of a membership with the AIM-DEEP program for those working with operators or service companies in the oil and gas industry?

Aminzadeh: With its hybrid structure, AIM-DEEP is not yet another university consortium. Its “BASE membership” provides cost-effective R&D on topics prioritized by sponsors. Other benefits include

  1. Quick access to experts on machine learning at UH-AIM-DEEP and its academic and vendor partners;
  2. Receiving software and other technical material on AI and data analytics;
  3. Crossing discipline boundaries within UH and other partners; and
  4. Filling the gap between the energy industry AI/data analytics needs and the capabilities available in other industries.

The “Individually Sponsored Projects,” or ISP membership, provides the additional benefits of exclusive access to intellectual properties while honoring data confidentiality.

For more information, visit https://aim-deep.petro.uh.edu or contact faminzad@centeral.uh.edu.