The delineation of heterogeneity and compartments within a reservoir is essential for field development and reserve estimation. In a complex reservoir in the Malay Basin, conventional seismic attributes are ineffective at characterizing the reservoir facies. We have developed a hybrid seismic waveform classification that combines standard unsupervised classification with a highly-interactive supervised classification. This combines the simplicity of unsupervised classification with the flexibility of supervised classification. The new tool successfully delineates previously unknown reservoir compartments, allowing us to plan for the field development and resource management. Introduction Characterizing reservoir heterogeneity is essential for optimum resource estimation and field development. Reservoir heterogeneity should be understood as early as possible in field development to design an appropriate drainage strategy that increases the efficiency of producing the reserves. As more complex reservoirs are targeted, it becomes more difficult to predict facies types and extents. The field involved in this study, which is in the Malay Basin, is especially complex due to compartmentalization, making it difficult to ascertain the reservoir boundaries and heterogeneity. The main reservoirs in the field are of excellent quality with porosity up to 35%. They are characterized by 15 to 20 m of sand fining upward that was deposited in prograding mouth bars in a shallow deltaic plain. In contrast, the reservoir of interest in this study is very thin, from 2 to 10 m thick, with a porosity of 15%. Conventional seismic attributes are unable to distinguish compartments in this reservoir, making it difficult to monitor the water injection and understand the cross-well injector-producer pressure response. Seismic data contains significant stratigraphic information that helps identify regions with similar reservoir facies. This information is contained within waveforms, which are small segments of seismic traces that could represent single reflections or patterns of interfering reflections. A waveform is a function of amplitude, phase, and frequency. Although individual waveforms lack inherent geologic meaning, modeling and clustering can help relate them to known geology (Poupon et al., 1999; Coleou, et al., 2003). However, realistic models require so many variables that modeled waveforms are highly non-unique and have little diagnostic value. As a result, waveform maps are always interpreted qualitatively. An effective and popular method for automatic pattern recognition that uses neural networks to analyze regions of similar waveform is the Kohonen Self-Organizing Feature Map, or KSOFM (Addy, 1997; Barnes and Laughlin, 2002). As with any method of unsupervised classification, KSOFM is driven entirely by the seismic data, and the number of representative waveforms (classes) must be pre-determined before starting the neural networks training. The resulting waveform facies map is therefore limited to a pre-defined number of classes, which usually do not correspond to the facies of greatest interest. In order to address this shortcoming, we develop a hybrid seismic waveform classification that combines the advantages of unsupervised and supervised classification, and which is better at delineating reservoir heterogeneity.

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This reference is for an abstract only. A full paper was not submitted for this conference. Abstract The time-domain Controlled Source Electromagnetic (tCSEM) method is emerging as a practical tool for oil and gas exploration on land and shallow marine environments (Wright et al., 2002; Ziolkowski et al., 2007). In particular, it can overcome the airwave interference problem faced by frequency-domain CSEM method in shallow water (Weiss, 2007). We have used a 1D broadband tCSEM inversion technique developed in-house. The forward problem is solved using the algorithm of Edwards (1997) while the inverse problem is solved using a variety of nonlinear parameter estimation methods (Meju, 1992, 1994). The inversion method generates either blocky sharp-boundary model or the smoothest model that fits the data recorded for different offsets and various transient times. In this work, a layered-earth resistivity model was constructed using the local borehole resistivity log and served as the test model (ground-truth). The parameters of this model were systematically varied and used to generate forward model responses enabling us to determine the effect of target depth, thickness and resistivity variations. The synthetic data for the ground-truth model were finally inverted to test the detection capability of tCSEM. The aim of our research was to evaluate the applicability of tCSEM technology in East Malaysia environment by studying the effect of target and water depth, target thickness, target resistivity variation, and target detection limits using a combined numerical modelling and inversion approach. The results of modeling and inversion studies suggest that the tCSEM method is capable of detecting hydrocarbon reservoirs in East Malaysia environment. The inversion responses become more significant when dealing with thick, strong resistivity, and shallow marine hydrocarbon target. From the case of target depth variation, the inversion responses capable to distinguish hydrocarbon target at difference depth. From this study, we capable to investigate in detail about the strength and limitation of this method in East Malaysia environment and this beneficial information can become a guideline for future feasibility study and tCSEM survey planning. Furthermore, this method also can be used as an alternative method to provide technical solution and reduce exploration risk especially in shallow marine environment.

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