Geophyical Insights – University Challenge Topics
Call for Abstracts
The following “Challenge Topics” are offered to universities who are part of the Paradise University Program. Those universities are encouraged to consider pursuing one or more of the topics below in their research work with Paradise® and related interpretation technologies. Students interested in researching and publishing on one or more of these topics are welcome to submit an abstract to Geophysical Insights, including an explanation of their interest in the topic. The management of Geophysical Insights will select the best abstract per Challenge Topic and provide a grant of $1,000 to each student upon the completion of the research work. Student(s) who undertake the research may count on additional forms of support from Geophysical Insights, including:
- Potential job interview after graduation
- Special recognition at the Geophysical Insights booth at a future SEG
- Occasional collaboration via web meeting, email, or phone with a senior geoscientist
- Inclusion in invitations to webinars hosted by Geophysical Insights on geoscience topics
Challenge Research Topics
Develop a geophysical basis for the identification of thin beds below classic seismic tuning
The research on this topic will investigate applications of new levels of seismic resolution afforded by multi-attribute Self-Organizing Maps (SOM), the unsupervised machine learning process in the Paradise software. The mathematical basis of detecting events below classical seismic tuning through simultaneous multi-attribute analysis – using machine learning – has been reported by Smith (2017) in an abstract submitted to SEG 2018. (Subsequently, the abstract has been placed online as a white paper resource). Examples of thin-bed resolution have been documented in a Frio onshore Texas reservoir, and in the Texas Eagle Ford Shale by Roden, et al., (2017). Therefore, the researcher is challenged to develop a better understanding of the physical basis for the resolution of events below seismic tuning vs. results from wavelet-based methods. Additional empirical results of the detection of thin beds are also welcomed. This approach has wide potential for both exploration and development in the interpretation of facies and stratigraphy and impact on reserve/resource calculations. For unconventional plays, thin bed delineation will have a significant influence on directional drilling programs.
Determine the effectiveness of ‘machine learning’ determined geobodies in estimating reserves/resources and reservoir properties
The Paradise software has the capability of isolating and quantifying geobodies that result from a SOM machine learning process. Initial studies conducted with the technology suggest that the estimated reservoir volume is approximately what is being realized through the life of the field. This Challenge is to apply the geobody tool in Paradise along with other reservoir modeling techniques and field data to determine the effectiveness of geobodies in estimating reserves. If this proves to be correct, the estimating of reserves from geobodies could be done early in the lifecycle of the field, saving engineering time while reducing risk.
Corroborate SOM classification results to well logs or lithofacies
A challenge to cluster-based classification techniques is corroborating well log curves to lithofacies. Up to this point, such corroboration has been an iterative process of running different neural configurations and visually comparing each classification result to “ground truth”. Some geoscientists (results yet to be published) have used bivariate statistical analysis from petrophysical well logs in combination with the SOM classification results to develop a representation of the static reservoir properties, including reservoir distribution and storage capacity. The challenge is to develop a methodology incorporating SOM seismic results with lithofacies determination from well logs.
Explore the significance of SOM low-probability anomalies (DHIs, anomalous features, etc.)
In addition to a standard classification volume resulting from a SOM analysis, Paradise also produces a “Probability” volume that is composed of a probability value at each voxel for a given neural class (neuron). This technique is a gauge of the consistency of a feature to the surrounding region. Direct Hydrocarbon Indicators (DHIs) tend to be identified in the Paradise software as “low probability” or “anomalous” events because their properties are often inconsistent with the region. These SOM low probability features have been documented by Roden et al. (2015) and Roden and Chen (2017). However, the Probability volume changes with the size of the region analyzed, and with respect to DHIs and anomalous features. This Challenge will determine the effectiveness of using the probability measure from a SOM result as a valid gauge of DHIs and set out the relationships among the optimum neural configuration, the size of the region, and extent of the DHIs.
Map detailed facies distribution from SOM results
SOM results have proven to provide detailed information in the delineation and distribution of facies in essentially any geologic setting (Roden et al., 2015; Roden and Santogrossi, 2017; Santogrossi, 2017). Due to the high-resolution output of appropriate SOM analysis, individual facies units can often be defined in much more detail than conventional interpretation approaches. Research topics should be related to determining facies distribution in different geological environments utilizing the SOM process, available well log curves, and regional knowledge of stratigraphy.
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Most Popular Papers
Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification