3 research outputs found
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Continuous learning of analytical and machine learning rate of penetration (ROP) models for real-time drilling optimization
Oil 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 expensePetroleum and Geosystems Engineerin
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Development and applications of a new system to analyze field data and compare rate of penetration (ROP) models
Improvements in data acquisition technology have enhanced rate of penetration (ROP) modeling capabilities. Modern logging tools are able to record more complete drilling datasets at a higher frequency, allowing for better understanding of the many variables that affect the drilling process. ROP models published in literature simplify drilling rate formulations by combining complex drilling factors into model coefficients. The lithology dependence of ROP model coefficients, as well as the model's performance evaluated based on different types of rocks, is a topic explored throughout this project. A data analysis software developed in Microsoft Excel VBA, named ROPPlotter, provides ROP field data visualization and comparison of different ROP models. Userforms offer great flexibility in selecting different sections of the well and in highlighting lithology changes. The program accomplishes data filtering by detecting data outliers in the original dataset and excluding them for a more meaningful analysis. Then, VBA coding is applied in order to produce neat-looking plots automatically, overcoming Excel’s poor standard plot formatting. Excel Solver is employed in determining coefficients of six ROP models: Bingham (1964), Bourgoyne & Young (1974), Winters-Warren-Onyia Roller Bit (1987), Hareland Drag Bit (1994), Hareland Roller Bit (2010) and Motahhari PDC Bit (2010). By studying how these coefficients change with varying rock formations, valuable information about each model's behavior is obtained. Plots containing field data and ROP models, in addition to parsed data utilized in model calculations, can be saved for future analysis with the click of a button. ROPPlotter is useful in conducting case studies for industry, such as slow ROP in a section of the well or slide drilling. Furthermore, it provides a systematic way to assess ROP model performance and aims to quantify the lithology dependence of ROP models and their coefficients. This exercise provides a means of determining which ROP model works best for a specific field application. Later, by using an average value of model coefficients calculated for a certain field, optimal values of parameters controlled at the rig floor (weight-on-bit, rotary speed, flow rate) are determined for a future well to be drilled on the same pad.Petroleum and Geosystems Engineerin