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Uncertainty in the transmitter position, theory error and insufficient model parameterization amongst various other factors can lead to significant correlated error in observed controlled source electromagnetic data. These errors come to light by an examination of the residuals after performing inversion. Since correlated error violates the assumption of independent data noise it can manifest in spurious structure in inverted models. We demonstrate this using both synthetic data and real data from Scarborough gas field, North West Australia. In this work we propose a method which uses a hierarchical Bayesian framework and reversible jump Markov chain Monte Carlo to account for correlated error. We find that this removes suspect structure from the inverted models and within reasonable prior bounds, provides information on the resolution of resistivity at depth.
We consider the simultaneous 3D CSEM inversion of data from a towed receiver array and a very coarse grid of stationary seabed receivers. The inversion results show that the shortoffset data from the towed receivers are effective at resolving a shallow resistor along the towlines. On the other hand, the data from the stationary seabed receivers have less noise and will resolve the 3D geometry of deeper resistive structure typical for a hydrocarbon reservoir. We discuss uncertainty contributions in the two receiver types, and contaminated the synthetic data with noise corresponding to realistic levels.
The data from seabed receivers used in 3D CSEM surveys can effectively resolve subsurface resistivity structure due to e.g. hydrocarbon accumulations, lithology, and salt. When the survey is acquired in frontier areas, the definition of prospects can be uncertain, or the survey may be intended to generate prospects related to larger hydrocarbon reservoirs. Costeffective coarse receiver grids can be used in such surveys to cover large areas. While giving good definition of deeper structure, such coarse receiver grids result in limited shortoffset data which is important to define shallow resistors.
Towed receiver systems that can be deployed in conjunction with a horizontal electric dipole source have recently been developed (Constable et al., 2012). The fixed-offset data from such receivers towed behind the vessel have been used for e.g. 2D mapping of shallow gas hydrates (Weitemeyer and Constable, 2010). The operational complexity of towing such equipment close to the seafloor typically limits the offset range, but the data could be very useful to complement the coarse-grid 3D CSEM survey technique described above.
In this paper we consider 3D inversion of CSEM data from a towed receiver array as well as stationary seabed receivers. We invert synthetic data that have been contaminated by noise at realistic levels. The model considered contains targets representing typical hydrocarbon reservoirs at various burial depths, as well as a large-scale very shallow thin resistor. The inversions are carried out using either data from the towed or stationary receivers alone, or we include data from both receiver types in combination. We discuss the effect on imaging for the three data combinations, and discuss the uplift from complementing the coarse-grid 3D CSEM survey data with towedreceiver data.
We carry out the two-dimensional inversion of marine controlled-source electromagnetic data from the SEG advance modeling program using MARE2DEM Software.We applied this inversion on three survey lines from the given data set to image the salt body and delineate thin hydrocarbon reservoirs that are present near the salt flanks.The inversion was unconstrained and did not use any a priori information about the salt body from the seismic imaging or nearby well logs. Despite the complex 3D structure of thesalt model, our inverted results agree well with the truemodel demonstrating the robustness of the method in imaging the reservoirs and their lateral extents without any prior information.
Presentation Date: Tuesday, October 16, 2018
Start Time: 9:20:00 AM
Location: Poster Station 15
Presentation Type: Poster