Vanessa Su Lee Goh

Collaborative Adaptive Filters for Online Knowledge Extraction and Information Fusion

We present a method for extracting information (or knowledge) about the nature of a signal. This is achieved by employing recent developments in signal characterisation for online analysis of the changes in signal modality. We show that it is possible to use the fusion of the outputs of adaptive filters to produce a single collaborative hybrid filter and that by tracking the dynamics of the mixing parameter of this filter rather than the actual filter performance, a clear indication as to the nature of the signal is given. Implementations of the proposed hybrid filter in both the real R and the complex C domains are analysed and the potential of such a scheme for tracking signal nonlinearity in both domains is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis; real world applications of the approach have been illustrated on electroencephalogram (EEG), radar and wind data.

Uncovering the Missing Data in the Gas Cloud with P-P Wave Imaging – a Deep Water OBN Survey from Southeast Asia Region

Summary The framework of reservoir modelling is the information and understanding derived from the 3D seismic data covering the area of interest. Uncertainties associated with the underlying seismic data potentially lead to the inability to draw clear conclusions as to the size and productivity of the reservoir. In the presence of low saturation gas bodies in the overburden sediments, the loss of signal strength, frequency bandwidth and the complex wave kinematics compound the challenge. We present a case study from a challenging deep-water oil field in southeast Asia. The project comprised of acquisition, processing, interpretation and QI groups working as an integrated team to meet the objectives of the project. The project utilized a multifaceted approach of combining ocean bottom node seismic data, optimizing preprocessing, building a high-resolution earth model, and using high-end imaging techniques, to significantly improve the overall quality of the seismic data beneath the large bodies of gas hydrates, clouds and free gas. The new data allows improved structural and stratigraphic interpretation providing a better understanding of the reservoir model and reducing the uncertainty related to the reservoir volume, connectivity and compartmentalization, thereby contributing significantly to the geological understanding of the field and influencing the future development decisions.

Application of FWI in Central Luconia, Malaysia

The Fxx field is located in the Central Luconia basin offshore Sarawak, with varying water depths from 80 to 95 meters. Hydrocarbon accumulations are found in clastic field at the shallower level as well as in the deeper carbonate build up. In the central part of the Fxx field, absorption and scattering by near surface channels appears to cause data degradation, highlighted by extraction of seismic amplitude . This near surface gas- filled channel system, which leaked from the F field, have caused prominent poor, wipe out data zone. In this paper, we present a case study of the use of FWI and tomographic model updating workflow, with the aim to improve the seismic imaging quality of the wipe out zone. Although such technique is not new to address this issue, we wish to demonstrate that this processing solution has not been accessed in this area. Picking accurate shallow RMO is generally very difficult on conventional seismic because of the limited usable offset range and resulting low effective fold. To overcome this limitation we inverted for shallow velocities using full waveform inversion. We performed simultaneous FWI inversion of velocity and eta. The FWI result suggested a low velocity anomaly at a location where we see an event with high amplitude in the migration section. This probably corresponds to a shallow gas in the channel body . The bright amplitudes shown on the left map view in Figure 2 corresponds very well with the channel bodies whereby sand deposited on the edges. After the FWI update, we merged the FWI model with the deeper model and proceeded with travel time tomography updating. Significant attention was given to the construction of a ‘geologically plausible’ velocity field in the deeper section. In the tomography update, the main geological interfaces are used as soft contrast to preserve the velocity break in the model. We have also included the dip information in the grid for the tomography inversion. An anisotropic model with incorporation of epsilon and delta was built in this manner. The final FWI tomography velocity model shows a network of shallow low velocity channels associated with gas that matches similar features in the reflection data. The resulting velocity model provides a better match to well logs, and better flattens migrated gathers, compared to the starting model. The final results will be shared, and the business impact will be discussed.