Continuous learning of analytical and machine learning rate of penetration (ROP) models for real-time drilling optimization

Simple item page
dc.contributor.advisorGray, Kenneth E., Ph. D.
dc.contributor.committeeMemberDaigle, Hugh
dc.contributor.committeeMemberPyrcz, Michael
dc.contributor.committeeMemberMillwater, Harry
dc.contributor.committeeMemberPanchal, Neilkunal
dc.creatorSoares, Cesar Mattos De Salles
dc.date.accessioned2021-06-21T17:03:45Z
dc.date.available2021-06-21T17:03:45Z
dc.date.created2018-08
dc.date.issued2018-08-16
dc.date.submittedAugust 2018
dc.date.updated2021-06-21T17:03:45Z
dc.description.abstractOil and gas operators strive to reach hydrocarbon reserves by drilling wells in the safest and fastest possible manner, providing indispensable energy to society at reduced costs while maintaining environmental sustainability. Real-time drilling optimization consists of selecting operational drilling parameters that maximize a desirable measure of drilling performance. Drilling optimization efforts often aspire to improve drilling speed, commonly referred to as rate of penetration (ROP). ROP is a function of the forces and moments applied to the bit, in addition to mud, formation, bit and hydraulic properties. Three operational drilling parameters may be constantly adjusted at surface to influence ROP towards a drilling objective: weight on bit (WOB), drillstring rotational speed (RPM), and drilling fluid (mud) flow rate. In the traditional, analytical approach to ROP modeling, inflexible equations relate WOB, RPM, flow rate and/or other measurable drilling parameters to ROP and empirical model coefficients are computed for each rock formation to best fit field data. Over the last decade, enhanced data acquisition technology and widespread cheap computational power have driven a surge in applications of machine learning (ML) techniques to ROP prediction. Machine learning algorithms leverage statistics to uncover relations between any prescribed inputs (features/predictors) and the quantity of interest (response). The biggest advantage of ML algorithms over analytical models is their flexibility in model form. With no set equation, ML models permit segmentation of the drilling operational parameter space. However, increased model complexity diminishes interpretability of how an adjustment to the inputs will affect the output. There is no single ROP model applicable in every situation. This study investigates all stages of the drilling optimization workflow, with emphasis on real-time continuous model learning. Sensors constantly record data as wells are drilled, and it is postulated that ROP models can be retrained in real-time to adapt to changing drilling conditions. Cross-validation is assessed as a methodology to select the best performing ROP model for each drilling optimization interval in real-time. Constrained to rig equipment and operational limitations, drilling parameters are optimized in intervals with the most accurate ROP model determined by cross-validation. Dynamic range and full range training data segmentation techniques contest the classical lithology-dependent approach to ROP modeling. Spatial proximity and parameter similarity sample weighting expand data partitioning capabilities during model training. The prescribed ROP modeling and drilling parameter optimization scenarios are evaluated according to model performance, ROP improvements and computational expense
dc.description.departmentPetroleum and Geosystems Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/86531
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/13482
dc.subjectDrilling optimization
dc.subjectRate of penetration (ROP) modeling
dc.subjectMachine learning
dc.subjectContinuous learning
dc.subjectCross-validation
dc.subjectPDC bits
dc.titleContinuous learning of analytical and machine learning rate of penetration (ROP) models for real-time drilling optimization
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentPetroleum and Geosystems Engineering
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
SOARES-DISSERTATION-2018.pdf
Size:
10.65 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.46 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.85 KB
Format:
Plain Text
Description:
Download

Collections

UT Electronic Theses and Dissertations