Morgan Sullivan

Event-Based Geostatistical Modeling: Description and Applications

Event-based methods provide unique opportunities to improve the integration of geologic concepts into reservoir models. This may be accomplished over a continuum of rule complexity from very simple geometric models to complicated dynamics. Even the application of simple rules, including few conceptual interactions based on an understanding of stratigraphic relationships and parametric geometries for event scale depositional and erosion features, have been shown to efficiently produce complicated and realistic reservoir heterogeneities. In more complicated applications, initial and boundary conditions from analysis of paleobathymetry and external controls on sediment supply and the event rules may be informed by process models. These models have interesting features that depart from typical geostatistical model; they demonstrate emergent behaviors and preserve all information at all scales during their construction. These models may be utilized to produce very realistic reservoir models and their unique properties allow for novel applications. These modeling applications include; impact of model scale, seismic resolvability, value of information, flow relevance of advanced architecture, iterative and rule-based conditioning to sparse well and seismic data, numerical analogs for architectural concepts, statistical analysis and classification of architectures, unstructured grid construction and utilization as training and visualization tools.

Computational intelligence for deepwater reservoir depositional environments interpretation

Abstract Predicting oil recovery efficiency of a deepwater reservoir is a challenging task. One approach to characterize a deepwater reservoir and to predict its producibility is by analyzing its depositional information. This research proposes a deposition-based stratigraphic interpretation framework for deepwater reservoir characterization. In this framework, one critical task is the identification and labeling of the stratigraphic components in the reservoir, according to their depositional environments. This interpretation process is labor intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher’s workload and to produce more consistent results, we have developed a novel methodology to automate this process using various computational intelligence techniques. Using a well log data set, we demonstrate that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.

Evolving finite state transducers to interpret deepwater reservoir depositional environments

Predicting oil recovery efficiency of deepwater reservoirs is a challenging task. One approach to characterize and predict the producibility of a reservoir is by analyzing its depositional information. In a deposition-based stratigraphic interpretation framework, one critical step is the identification and labeling of the stratigraphic components in the reservoir according to their depositional information. This interpretation process is labor intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigraphers workload and to produce more consistent results, this research developed a methodology to automate this process using various computational intelligent techniques. Using a well log data set, we demonstrated that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.

Seismic stratigraphy and seismic geomorphology of a slope depositional environment — Case study from offshore Angola, West Africa

Deep-water Slope and basin floor deposition characterizes the stratigraphy of offshore Angola, West Africa. The section can be subdivided into two parts. A lower part characterized by nearly horizontal slope at the time of deposition, and an upper part characterized by a slope of ~1.5 degrees. The lower part can be described as a low accommodation section wherein multiple channels closely spaced are observed. In contrast, the upper part can be described as a high accommodation section wherein channels are observed to be widely separated by slope deposits. Turbidite channels within the low accommodation section are commonly not deeply incised into the substrate and are of relatively low relief from channel axis to marginal levee crest. In the high accommodation section similar channels are observed, however larger slope valleys are also present. Both organized and disorganized channel complexes are observed in both sections. Conspicuous by their absence are mass transport deposits. These are observed only along the flanks of larger slope valleys and appear to be restricted in their distribution to valley margins.