Fast history matching of time-lapse seismic and production data for high resolution models

Integrated reservoir modeling has become an important part of day-to-day decision analysis in oil and gas management practices. A very attractive and promising technology is the use of time-lapse or 4D seismic as an essential component in subsurface modeling. Today, 4D seismic is enabling oil companies to optimize production and increase recovery through monitoring fluid movements throughout the reservoir. 4D seismic advances are also being driven by an increased need by the petroleum engineering community to become more quantitative and accurate in our ability to monitor reservoir processes. Qualitative interpretations of time-lapse anomalies are being replaced by quantitative inversions of 4D seismic data to produce accurate maps of fluid saturations, pore pressure, temperature, among others. Within all steps involved in this subsurface modeling process, the most demanding one is integrating the geologic model with dynamic field data, including 4Dseismic when available. The validation of the geologic model with observed dynamic data is accomplished through a "history matching" (HM) process typically carried out with well-based measurements. Due to low resolution of production data, the validation process is severely limited in its reservoir areal coverage, compromising the quality of the model and any subsequent predictive exercise. This research will aim to provide a novel history matching approach that can use information from high-resolution seismic data to supplement the areally sparse production data. The proposed approach will utilize streamline-derived sensitivities as means of relating the forward model performance with the prior geologic model. The essential ideas underlying this approach are similar to those used for high-frequency approximations in seismic wave propagation. In both cases, this leads to solutions that are defined along "streamlines" (fluid flow), or "rays" (seismic wave propagation). Synthetic and field data examples will be used extensively to demonstrate the value and contribution of this work. Our results show that the problem of non-uniqueness in this complex history matching problem is greatly reduced when constraints in the form of saturation maps from spatially closely sampled seismic data are included. Further on, our methodology can be used to quickly identify discrepancies between static and dynamic modeling. Reducing this gap will ensure robust and reliable models leading to accurate predictions and ultimately an optimum hydrocarbon extraction.