Abstract
An electromagnetic (EM) technique is employed in seabed logging (SBL) to detect offshore hydrocarbon-saturated reservoirs. In risk analysis for hydrocarbon exploration, computer simulation for subsurface modelling is a crucial task. It can be expensive and time-consuming due to its complicated mathematical equations, and only a few realizations of input-output pairs can be generated after a very lengthy computational time. Understanding the unknown functions without any uncertainty measurement could be very challenging as well. We proposed model calibration between a stochastic process and computer experiment for magnitude versus offset (MVO) analysis. Two-dimensional (2D) Gaussian process (GP) models were developed for low-frequencies of 0.0625–0.5 Hz at different hydrocarbon depths to estimate EM responses at untried observations with less time consumption. The calculated error measurements revealed that the estimates were well-matched with the computer simulation technology (CST) outputs. Then, GP was fitted in the MVO plots to provide uncertainty quantification. Based on the confidence intervals, hydrocarbons were difficult to determine especially when their depth was 3000 m from the seabed. The normalized magnitudes for other frequencies also agreed with the resulting predictive variance. Thus, the model resolution for EM data decreases as the hydrocarbon depth increases even though multi-low frequencies were exercised in the SBL application.
Highlights
The electromagnetic (EM) imaging technique for geophysical applications is categorized as a geo-electrical method family which can be grouped into two types, active and passive [1]
Due to rapid development of the EM technique, it is a well-established surveying technique for offshore hydrocarbon exploration based on the technology of horizontal source
In order to seek the most favorable balance between modelling accuracy associated with uncertainty quantification in the forward modelling and computational cost involved in the computer simulations, we propose a methodology of model calibration between a well-known stochastic process, namely Gaussian process (GP) regression, and computer experiments using computer simulation technology (CST) software in the magnitude versus offset (MVO) analysis
Summary
The electromagnetic (EM) imaging technique for geophysical applications is categorized as a geo-electrical method family which can be grouped into two types, active and passive [1]. The marine controlled-source electromagnetic (CSEM) surveying technique is classified as an active method due to the character of its EM source. Marine CSEM is known as seabed logging (SBL). SBL is an application of the EM technique for marine hydrocarbon prospecting in the deep offshore environment. This application has become a promising tool to detect, characterize, and map offshore hydrocarbon-filled reservoirs. Due to rapid development of the EM technique, it is a well-established surveying technique for offshore hydrocarbon exploration based on the technology of horizontal source
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