3D CSEM can play vital role in offshore exploration

3D controlled source electromagnetic (CSEM) surveys map resistive bodies in the subsurface - fluid-driven parameters that correlate strongly with the fluid content and saturation of hydrocarbons where the larger the resistive body, the greater the response. Whereas most prospects generally have higher uncertainty in probability of success to start with based on seismic interpretation alone, adding EM data will enable better fluid predictions. When trying to optimize and prioritize a portfolio of prospects, 3D CSEM data provides operators with the ability to polarize their portfolio and identify areas with larger hydrocarbon accumulations where they are more likely to be economically viable.

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Illustrative example of a portfolio before 3D CSEM. (All images courtesy EMGS)
Data provides operators improved probability of economic success

Vincent Vieugue
Jason Tinder

EMGS

There is no doubt that seismic data is highly effective in providing operators with structural and stratigraphic information (trap and reservoir potential) as well as volume estimation parameters such as gross rock volumes.

Yet seismic continues to have difficulties in detecting fluid content and the potential hydrocarbon volumes within the reservoir. There is subsequently a danger of operators identifying a prospect with what appears to be strong seismic, amplitude versus offset and geological potential but failing to predict the fluids. With a significant number of wells failing due to uncertainties on fluid content, this is a significant information gap.

It is against this backdrop that 3D controlled source electromagnetic (CSEM) surveys have a vital role to play in the offshore exploration workflow – accurately predicting fluids and field volumes and delivering improved probability of economic success for operators.

Predicting field volumes

3D CSEM surveys map resistive bodies in the subsurface – fluid-driven parameters that correlate strongly with the fluid content and saturation of hydrocarbons where the larger the resistive body, the greater the response.

Whereas most prospects generally have higher uncertainty in probability of success to start with based on seismic interpretation alone, adding EM data will enable better fluid predictions.

When trying to optimize and prioritize a portfolio of prospects, 3D CSEM data provides operators with the ability to polarize their portfolio and identify areas with larger hydrocarbon accumulations where they are more likely to be economically viable. This brings with it significant savings in drilling costs as well as ultimately more profitable fields.

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The Wisting and Hanssen oil discoveries are clearly associated with strong resistive anomalies (red areas) from the 3D CSEM data. The well next to Hanssen going through a low resistive area (blue) is dry. (Seismic data courtesy TGS)

Portfolio polarization

The impact of portfolio prioritization using 3D CSEM can be seen in the seismically derived prospects in the first two graphs.

The first represents a portfolio of prospects with the vertical axis representing probability of success and the horizontal access estimated recovery volumes by a million barrels of oil equivalent (MMboe). The second shows this same portfolio after the application of 3D CSEM where prospects with a resistive anomaly see an improvement in estimated reserves and/or probability of success and the remainder are near the economic threshold or found to be non-commercial.

The theory of portfolio polarization is to high-grade commercial prospects, disqualify non-commercial prospects, and drill high impact discoveries first. As in this example, the use of 3D CSEM with seismic, petrophysical, and geologic data enhances subsurface understanding by limiting the range of possible geological scenarios.

3D CSEM's sensitivity to net rock volume and pore fluid can result in a significant reduction in uncertainty since prospect area and net pay are associated with the highest uncertainties in reserves estimation, especially in frontier exploration areas. This dramatic difference in portfolios leads to a better classification of prospects through the downgrading or upgrading of the probability of finding hydrocarbons, and an improvement in the evaluation of the size of the accumulation.

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