- Contribute to developing the next generation of reservoir formation evaluation and reservoir modelling technologies.
- Gain experience of using industry software and evaluation techniques for downhole log data.
- Work with experts in the field of geologically-based petrophysics on research that offers career paths in industry and academia.
In hydrocarbon exploration, subsurface formations are often evaluated by running a number of borehole sensors and recording various responses (e.g. natural radioactivity, resistivity, acoustic velocity) that can be related to the volumetric properties of the formation (e.g. porosity, mineralogical volumes, water and hydrocarbon saturations) via defined sensor response equations. In turn, inferences are made between these evaluated properties and dynamic reservoir properties (e.g. permeability) that are subsequently used to develop reservoir models.
Petrophysicists often make assumptions about the mineral composition and the fluids present in the pore-space when analysing the log data, and the nature of the models that relate the distribution of fluid and pore structure. These assumptions then determine the nature of the relationship between formation properties and individual sensor responses (e.g. velocity and acoustic logs). The acquisition and analysis of core samples can provide additional information on mineralogy and formation properties and it is common practice to seek to all available data to define a single interpretation of how formation volumetric properties vary with depth. However, this ‘single interpretation’ approach tends to ignore the uncertainties associated with the computed formation volumetric properties, and in particular how the different uncertainties associated with the various analysis methods (e.g. log/sensor responses or core data) interplay in the final interpretation.
The aim of this project is to develop an integrated multi-sensor and core inversion process. The results of such an inversion can be used to characterise formation volumetric reservoir properties based on all available observables and the corresponding uncertainties. The simultaneous consideration of all available data will help to better constrain dynamic reservoir properties and reduce exploration risk. The project will initially focus on shaly-sandstone formations, but the intention is that the interpretation workflows developed by this project will also be extended to other formation evaluation challenges.
This project will:
- Explore how changes in formation properties are reflected in petrophysical logs in shaly-sandstone formations, and how special core analysis measurements can be used to constrain interpretation models (e.g. resistivity-water saturation equations).
- Implement Bayesian simultaneous inversion methods to define formation volumetric properties (and associated uncertainties) on a depth by depth basis.
The project will focus on core and log data acquired for a shaly-sandstone formation where borehole logging and special core analysis data is available. Initially, work will focus on interpreting these data using published petrophysics workflows to define volumetric properties. This will establish a reference dataset and help identify weaknesses in the currently used methods.
The student will then explore how Bayesian concepts may be employed to evaluate the appropriateness of different interpretation models (e.g. shaly-sandstone resistivity models), before developing and applying simultaneous inversion procedures integrating a variety of observations.
Finally, the work will focus on reviewing the importance of different interpretation parameters or different logging/sensor responses in constraining the uncertainties associated with the quantification of the formation volumetric properties.
This work will facilitate an improved understanding of the uncertainties associated with characterising the storage and flow characteristics of shaly-sandstone formations, and build capabilities to predict the expectation values and probability distributions associated with key properties used in reservoir models. Such information is key in reducing risk in hydrocarbon exploration.
Training and Skills
CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research themes.
The project specific training will include sessions on petrophysical quantities and the relation to geophysical observation, statistical methods and inverse theory. Depending on the background of the student, further training will be provided on programming and numerical methods.
Year 1: Perform individual analyses of a petrophysical log using standard techniques with a view to identify inconsistencies and limitations. Experiment with probabilistic analysis methods and explore petrophysical formulations that can be used to integrate logs from different sensors.
Year 2: Implement integrated log analysis methods that combines various logging tools in a self-consistent manner. Assess efficiency of the new methodology using synthetic borehole logs.
Year 3: Apply methodology to real borehole logs, using the results from the synthetic experiments to identify in how far the chosen petrophysical formulation can be applied. Quantify uncertainty on the estimated petrophysical parameters and compare with traditional analysis of the same core material.
Partners and collaboration (including CASE)
Dr Moorkamp’s research focuses on combining different geophysical data types in a joint inversion to improve our understanding of the subsurface. Prof. Lovell applies geologically-based petrophysics to understanding conventional and unconventional oil and gas reservoirs. Dr Pritchard has over 30 years of industry experience, including 10 years as Head of Petrophysics for BG plc. It is proposed that this work would be undertaken in collaborative partnership with operating companies such as Shell, BP and Nexen to secure access to high quality core and log data that would facilitate the development of this work, and gain industrial experience through placements.
Please contact Max Moorkamp, University of Leicester, firstname.lastname@example.org