Toumi et al., 2023 - Google Patents
Optimization of Rate of Penetration through Regression ModelToumi et al., 2023
- Document ID
- 9699737309208018113
- Author
- Toumi S
- Bouyahi R
- Publication year
- Publication venue
- Spring School on Control & Inverse Problems
External Links
Snippet
The primary objective of this study is to enhance drilling efficiency and reduce operational costs for drilling tools by optimizing the Rate of Penetration (ROP). To achieve this, three optimization methods are compared in this chapter. Mathematical models are developed …
- 238000005457 optimization 0 title abstract description 23
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B41/00—Equipment or details not covered by groups E21B15/00 - E21B40/00
- E21B2041/0028—Fuzzy logic, artificial intelligence, neural networks, or the like
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
- E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
- E21B44/005—Below-ground automatic control systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B41/00—Equipment or details not covered by groups E21B15/00 - E21B40/00
- E21B41/0092—Methods relating to program engineering, design or optimisation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Al-Abduljabbar et al. | Application of artificial neural network to predict the rate of penetration for S-shape well profile | |
Alsaihati et al. | Use of machine learning and data analytics to detect downhole abnormalities while drilling horizontal wells, with real case study | |
Klyuchnikov et al. | Data-driven model for the identification of the rock type at a drilling bit | |
Moraveji et al. | Drilling rate of penetration prediction and optimization using response surface methodology and bat algorithm | |
Lashari et al. | Drilling performance monitoring and optimization: a data-driven approach | |
Bello et al. | Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art | |
Shahani et al. | Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA | |
Wang et al. | Application of real-time field data to optimize drilling hydraulics using neural network approach | |
Dashevskiy et al. | Application of neural networks for predictive control in drilling dynamics | |
Elkatatny | Real-time prediction of rate of penetration while drilling complex lithologies using artificial intelligence techniques | |
Al-Sudani | Real-time monitoring of mechanical specific energy and bit wear using control engineering systems | |
Payette et al. | A real-time well-site based surveillance and optimization platform for drilling: Technology, basic workflows and field results | |
Hegde et al. | Real time prediction and classification of torque and drag during drilling using statistical learning methods | |
Delavar et al. | Optimization of drilling parameters using combined multi-objective method and presenting a practical factor | |
Wang et al. | Drilling hydraulics optimization using neural networks | |
Saadeldin et al. | Intelligent model for predicting downhole vibrations using surface drilling data during horizontal drilling | |
Batruny et al. | Drilling in the digital age: an aproach to optimizing ROP using machine learning | |
Zanjani et al. | Data-driven hydrocarbon production forecasting using machine learning techniques | |
Zhan et al. | Hybrid physics-field data approach improves prediction of ROP/drilling performance of sharp and worn PDC bits | |
Said et al. | Theoretical development of a digital-twin based automation system for oil well drilling rigs | |
Alsaihati et al. | Determining severity of lateral and torsional downhole vibrations while drilling surface holes using three machine learning techniques | |
Ibrahim et al. | Real-time prediction of in-situ stresses while drilling using surface drilling parameters from gas reservoir | |
Elahifar et al. | A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf | |
Toumi et al. | Optimization of Rate of Penetration through Regression Model | |
Amadi et al. | Evaluation of derived controllable variables for predicting rop using artificial intelligence in autonomous downhole rotary drilling system |