This course will concentrate on three contrasting methods: surface seismic, marine controlled source electromagnetic (CSEM) and well-log data.
Improved reservoir management and production optimisation demands require accurate characterisation of reservoir properties and their changes through time. Advances in geophysical data acquisition and interpretation have led to significant improvements in the remote imaging of earth structure and properties. However, when only a single data type is considered, ambiguities in the interpretation can remain. Integration of disparate geophysical data types allows the strengths of each to be exploited. This course will concentrate on three contrasting methods: surface seismic, marine controlled source electromagnetic (CSEM) and well-log data, and will illustrate approaches to integrating these complementary sources of information to exploit the strengths of each, with the goal of providing estimates of rock and fluid properties with greater confidence than from any single data type.
Prerequisites (Knowledge/Experience/Education Required)
The course is designed to be followed by anyone with a geophysics background. Familiarity with EM methods would be useful.
The course will be presented in 5 broad sections, to be presented over the course of a day:
Background and motivation: This section discusses the motivation behind data integration and briefly reviews the approaches that can be adopted. Integration approaches fall into two main categories: Those used to improve the imaging of structure, and those used to determine rock and fluid properties at a reservoir scale.
- Technology: this section will present an overview of the geophysical methods to be applied.
- CSEM/MT technology: A summary of marine EM methods, including acquisition and interpretation, will be given although basic familiarity with the technology will be assumed.
- Seismic: An understanding of seismic quantitative interpretation will be assumed.
- Rock Physics:Rock physics is key to integrated interpretation for two key reasons.Firstly it provides a link between reservoir scale rock and fluid properties and the geophysical scale measurements used to measure them.Secondly it provides a link between the contrasting physics of the EM and seismic methods. This section will include discussion of the rock physics relationships required to simultaneously describe measurements made using different physical phenomena at a range of scales, and the impact of electrical anisotropy and its description.
- Sensitivity analysis: A key component of any integrated study is a sensitivity analysis to determine, for a given geological environment, what data type/attribute, or combination of such attributes is required to answer the geophysical question posed. This section will investigate the controls on sensitivity in a range of reservoir settings.
- Methods and workflows: There are numerous approaches to data integration depending on the problem to be solved.In this section two approaches will be presented and the pros and cons of each discussed:
- Integrated interpretation: In this approach seismic and EM data are inverted seperately, to provide measurements of impedence and resistivity. These physical results are then combined under a consistent rock physics framework derived from well log anaylysis, to provide an interpretation of rock and fluid properties.
- Joint inversion: In a joint inversion both steps of the integrated interpretation approach are combined in a single algorithm, which inverts seismic and EM data directly for the rock and fluid properties of interest. Rock physics relationships are explicitly included within the inversion algorithm.
- Challenges and case studies:In the final section, the ideas presented during the course will be illustrated using a variety of case studies.
At the end of the course attendees should be able to:
- Explain the benefits of an integrated geophysical approach to reservoir characterisation.
- Identify situations where data integration can provide improved results over those achieved when only one data type is considered.
- Select thegeophysical attribute or combination of attributes to address a reservoir characterization problem.