Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Drilling performance is becoming the top differentiator in the market, especially in highly competitive US land basins. This drives operators to input more energy into the system with higher rev/min, flow, and weight on bit (WOB). In many cases this energy is transformed into drilling dysfunction (shock and vibration [S&V]) instead of higher rate of penetration (ROP). To minimize drilling dysfunction and maximize energy efficiency, a fully digital and closed-loop workflow was developed. The workflow begins with use of a proprietary at-bit sensor, which captures high-resolution at-bit measurements of three-axis acceleration, torsional vibration, and rev/min. The tool is placed inside any existing bit and does not require an additional sub, thus not introducing extra connections, bottomhole assembly (BHA) integrity risk, and/or length. The high-resolution data is automatically processed and analyzed to reveal critical drilling dynamics, which are replicated in a digital model. This creates a highly accurate virtual environment in which to evaluate bit design, eliminating the cost and risk of real-world trial and error, enabling efficient, fit-for-purpose introduction of new technologies. The new digital workflow was first used in a challenging, interbedded application in East Texas. High frequency at-bit measurements were used to develop a new bit and data-driven operating parameter roadmaps. These yielded improved drilling performance, with 69% higher ROP, better dull conditions, and 67% lower vibrations locally, when compared to the direct operator offsets. The workflow was then used in a curve/lateral application in the Permian Basin, known for challenging S&V conditions. Key performance targets were single-run to total depth (TD), high dogleg severity (DLS) output in the curve, and record ROP in the lateral. A new bit was designed, and recommended drilling parameters were defined within the data-driven virtual environment. Field testing confirmed the model’s predictions, with a 10% average increase in ROP. High-resolution at-bit downhole measurements coupled with digital simulation capabilities have led to development of a new workflow driving the evolution of drill bit design. Instead of the traditional trial and error approach that costs resources, time, and performance, the new workflow with data-enhanced digital modelling and virtual drilling environment enables increased confidence, optimized solutions, and decreased cycle time.
Drilling performance is becoming the top differentiator in the market, especially in highly competitive US land basins. This drives operators to input more energy into the system with higher rev/min, flow, and weight on bit (WOB). In many cases this energy is transformed into drilling dysfunction (shock and vibration [S&V]) instead of higher rate of penetration (ROP). To minimize drilling dysfunction and maximize energy efficiency, a fully digital and closed-loop workflow was developed. The workflow begins with use of a proprietary at-bit sensor, which captures high-resolution at-bit measurements of three-axis acceleration, torsional vibration, and rev/min. The tool is placed inside any existing bit and does not require an additional sub, thus not introducing extra connections, bottomhole assembly (BHA) integrity risk, and/or length. The high-resolution data is automatically processed and analyzed to reveal critical drilling dynamics, which are replicated in a digital model. This creates a highly accurate virtual environment in which to evaluate bit design, eliminating the cost and risk of real-world trial and error, enabling efficient, fit-for-purpose introduction of new technologies. The new digital workflow was first used in a challenging, interbedded application in East Texas. High frequency at-bit measurements were used to develop a new bit and data-driven operating parameter roadmaps. These yielded improved drilling performance, with 69% higher ROP, better dull conditions, and 67% lower vibrations locally, when compared to the direct operator offsets. The workflow was then used in a curve/lateral application in the Permian Basin, known for challenging S&V conditions. Key performance targets were single-run to total depth (TD), high dogleg severity (DLS) output in the curve, and record ROP in the lateral. A new bit was designed, and recommended drilling parameters were defined within the data-driven virtual environment. Field testing confirmed the model’s predictions, with a 10% average increase in ROP. High-resolution at-bit downhole measurements coupled with digital simulation capabilities have led to development of a new workflow driving the evolution of drill bit design. Instead of the traditional trial and error approach that costs resources, time, and performance, the new workflow with data-enhanced digital modelling and virtual drilling environment enables increased confidence, optimized solutions, and decreased cycle time.
Active petrophysical geosteering in reservoir formations has become a very promising practice and commonplace to improve express formation evaluation and maximize hydrocarbon production from reservoirs. The present study showcases the effectiveness of utilizing artificial intelligence (AI) framework to process resistivity logging while drilling (LWD) data for the purpose of real-time optimizing well trajectories and maximizing hydrocarbon production in pay zones during drilling horizontal section. We introduce a novel framework that automatically adjusts planned well trajectories during horizontal drilling. The framework takes the planned wellbore trajectory, reservoir model porosity, and ultra-deep resistivity LWD data as input. Hydrocarbon saturation volume is then calculated using the Archie equation. Subsequently, optimization algorithms correct the planned trajectory to maximize wellbore production using hydrocarbon saturation volume. The framework delivers an optimal wellbore trajectory performing real-time formation evaluation, guiding the drill bit through highly saturated pay zones. The proposed framework was tested on a 2D synthetic dataset using various optimization algorithms, including reinforcement learning algorithms for continuous action spaces (PPO, DDPG, TwinDDPG) and Q-learning and evolutionary algorithms. The evolutionary group of optimization algorithms achieved the highest efficiency with baseline hyperparameter settings, improving cumulative oil saturation per drilled meter by up to 32.5%. Reinforcement learning algorithms needs to be further explored because they have promising results but still high computational complexity. The evolutionary algorithm was then verified on a 3D Groningen field dataset, determining optimal hyperparameters such as differential evolution strategy, population size, mutational constant, and maximum number of iterations. The framework improved cumulative oil saturation per drilled meter by up to 6%, significantly increasing the average number of penetrated hydrocarbon-saturated pay zones along the drilling path. The developed AI-based framework presents an innovative approach for real-time automatic correction of well drilling trajectories, maximizing well productivity. This method can significantly aid in the interpretation and optimization of decision-making related to geosteering.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.