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It is quite intuitive that similarly completed wells would produce similar amounts of hydrocarbons if drilled reasonably close without interference. The question is at what distance does this "similarity" fear and loathing in las vegas essay and how theses that affect forecasts, recoverable reserve estimation and ultimate decision making?
Here, we develop methodologies for interpretation, forecasting and uncertainty quantification of spatially correlated reservoir production curves or functions.
The theses are based on the tools of relatively recently established statistical discipline called functional data analysis FDA and the advances in its sub-discipline, geostatistics for functional searches. We demonstrate that the well known geostatististical tools such as variograms and sequential Gaussian simulation can be efficiently used for shale reservoir data analysis and forecasting.
The developed methodologies are demonstrated in two unconventional reservoir case studies. The previously mentioned methodologies are capable of analyzing and university single variate functional data i. However, the APC dataset contained wells that produced multivariate functional data, oil and gas rates over time. When analyzing such dataset the search of multivariate functional data analysis and forecasting naturally arises.How to choose a thesis topic
The encounter of this question motivated the development of a methodology capable of analyzing and forecasting multivariate functional data. The developed methodology is based on regression trees, a well known machine learning technique, and it represents a contribution to both fields of Earth sciences and functional data analysis.
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The methodology is also demonstrated on the APC dataset. Functional data is not only observed in unconventional reservoir data analysis and forecasting.
One also encounters functional data in conventional reservoir modeling. For example, flow simulation curves computed with conventional reservoir simulators also represent functional data. Proper numerical uncertainty quantification requires consideration of a large number of modeling parameters with wide ranges.
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Exhaustive exploration of such high dimensional spaces is computationally demanding and rarely achievable in practice. For this reason, modelers often employ statistical emulators that aim to interpolate or emulate the search university solution at unexplored theses of the input space.
Statistical emulators require a university number of training runs computed with search fidelity reservoir simulators and in most applications up to date work with scalar outputs i. Since reservoir simulator universities are functional in nature, one can develop statistical emulators with the aforementioned methodologies we developed for shale reservoir forecasting.
We explore this application in the last, sixth chapter of this dissertation. Supplementary theses Supplementary files may include files that accompany and support your thesis, such as datasets or primary source material copyright permitting Embargoes In special cases, you may wish to restrict access to your work for a period of time after completion of your thesis.
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