Data Driven Approaches to Model Building: Applications to Energy Industries

George Box’s famous quote “All models are wrong, but some are useful” is now widely known. Mathematical models can be built based on a combination of first principles and available data. The focus of this work is on the application of data-driven modelling approaches in two specific instances of problems in upstream (oil & gas extraction) and downstream (refining & chemicals) industries, namely (a) cementing of wells drilled for production of oil and gas from unconventional resources, such as shales; and (b) design of robust control-relevant models for oil refineries and chemical plants. Shale gas production from horizontal wells faces potential problems related to gas leakage from the cemented annulus of the well into the air and water reserves, with obvious environmental and productivity implications. Whether a well will leak or not depends on several factors, related to cement composition and preparation, the cementing process, well conditions, and others. A model would be useful in assessing ahead of time whether a cementing job will produce a non-leaking well or not. Such a model could be based on first principles, but would be extremely complicated. Alternatively, as done in this work, a model can be built using multivariate statistics and available data from several leaking and non-leaking wells, cemented under different enough scenarios. The model built has 35 input variables (in the broad categories of casing properties, cement and drilling mud properties, and operating conditions) and manages to correctly classify with confidence 81% of wells as leaking or non-leaking in cross-validation tests. An advanced control system relies on a good control-relevant model that is not merely a good approximation of the actual process under control but also satisfies additional properties necessary for controller design. Control-relevant models are typically identified through industrial experiments whose design is considerably more involved than standard design for parameter estimation. The focus of this study is how to design control-relevant identification experiments when elements of the model are already known. A new theoretical framework is developed and its significant advantages over standard methods are illustrated through numerical simulations. Several possibilities for future development are suggested.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Houston

Year dc:date.issued

2016

Author dc:creator

• Panjwani, Shyam

Advisor dc:contributor.advisor

• Nikolaou, Michael

Committee members dc:contributor.committeemember

• Grabow, Lars C.
• Palmer, Jeremy C.
• Qin, Guan
• Darby, Mark L.

• Data-driven modeling
• Energy Industries

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/5411

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/5411