Using Artificial Intelligence Techniques in Modeling and Predicting the Rheological Behavior of Nano-Based Drilling Fluids

Gasser, Moamen; Mahmoud, Omar; Ibrahim, Fatma M.; Abadir, M. F.

doi:10.1115/omae2021-63749

Cited by 5 publications

(1 citation statement)

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“…Geothermal energy is produced by drilling wells into these reservoirs and using circulating fluids that are either injected into or taken from the geothermal reservoirs to capture the heat of the Earth [149]. Notably, drilling methods used in oil and geothermal wells are comparable [150]. As a result, geothermal well drilling techniques are developed based on improvements in drilling technology within the oil industry [151].…”

Section: Utilizing Nanomaterials In the Drilling Operations Of Deep G...mentioning

confidence: 99%

A Comprehensive Review of Nanotechnology Applications in Oil and Gas Well Drilling Operations

Alkalbani,

Chala

2024

Energies

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The field of nanotechnology has shown promise in addressing major problems and improving drilling effectiveness. An overview of the difficulties encountered during oil and gas well drilling operations and the demand for creative solutions opens the debate. This review explores how nanotechnology is transforming the oil industry and enhancing performance as a whole. The evaluation of the uses of nanotechnology for better oil recovery, real-time monitoring, innovative materials, drilling fluids, and reservoir characterization are extensively discussed in this review. The primary function of additives is to improve the fundamental characteristics of drilling fluids. The variety of fluid additives available is a reflection of the complex drilling–fluid systems that are currently being used to enable drilling in increasingly difficult subsurface conditions. Common additives used in water- and oil-based drilling fluids include lubrication, shale stability, pH regulation, filtration control, rheology control, viscosification, and pH regulation. Drilling fluids frequently contain filtration control additives such as starch, polyanionic cellulose (PAC), carboxymethyl cellulose (CMC), and nanoparticles (NP). Commonly used rheology-modifier additives are xanthan gum, carboxymethyl cellulose, guar gum powder, and, more recently, salt-responsive zwitterionic polymers that were used as viscosifiers to water-based drilling fluids. The three main additives that regulate pH are citric acid monohydrate, potassium hydroxide, and sodium hydroxide. Additives that stabilize shale, such as potassium and sodium salts and asphaltenes, are often used. A wide range of materials are included in the category of lubricating additives, including polymers, asphaltenes, glass beads, oils of various grades, and oil-surfactants. Various fibrous materials, including wood, cotton, fibrous minerals, shredded tires from vehicles, and paper pulp, are used as additives to control circulation. Furthermore, shredded cellophane, bits of plastic laminate, plate-like minerals like mica flakes, granulated inert materials such as nut shells, and nano-polymers are used in wellbores to reduce fluid loss. The incorporation of nanoparticles into drilling fluids has produced upgraded fluids with better features, including improved lubricity, thermal stability, and filtering capacities. These developments aid in lowering friction, enhancing wellbore stability, and enhancing drilling efficiency. This paper also emphasizes how nanotechnology has made enhanced drilling equipment and materials possible. Drilling equipment’s longevity and performance are increased by nanocomposite materials that have been reinforced with nanoparticles due to their improved mechanical strength, wear resistance, and thermal stability. Advanced reservoir characterisation tools, including nanoparticle tracers and nanoscale imaging methods, can help locate the best drilling sites and increase production effectiveness. On the other hand, nanofluids and nanoemulsions can potentially increase oil recovery because they enhance fluid mobility, lower interfacial tension, and alter rock wettability. Although nanotechnology has many advantages, there are also issues that need to be resolved. For an implementation to be effective, factors including nanoparticle stability, dispersion, and potential environmental effects must be carefully taken into account. This review highlights the need for future research to create scalable manufacturing procedures, improve nanoparticle behaviour, and determine nanomaterials’ long-term environmental effects. In conclusion, this in-depth analysis illustrates the use of nanotechnology in transforming the process of drilling oil and gas wells.

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Section: Utilizing Nanomaterials In the Drilling Operations Of Deep G...mentioning

confidence: 99%

A Comprehensive Review of Nanotechnology Applications in Oil and Gas Well Drilling Operations

Alkalbani,

Chala

2024

Energies

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Addressing Diverse Petroleum Industry Problems Using Machine Learning Techniques: Literary Methodology─Spotlight on Predicting Well Integrity Failures

2022

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Artificial intelligence (AI) and machine learning (ML) are transforming industries, where low-cost, big data can utilize computing power to optimize system performance. Oil and gas (O&G) fields are getting mature, where well integrity (WI) problems become more common and field operations are now more challenging. Hence, they are good candidates for transformation due to the low cost of data storage, highlighting the oil market decline, along with dynamic risk posed during operations. This paper is presenting a comprehensive compilation of different ML applications in diverse disciplines of the petroleum industry. The pool of AI and ML with respect to different areas of applications along with publication years has been categorized. The main focus of this study is classifying well integrity failures where the authors found that the potential of AI and ML in predicting well integrity failures has not been efficiently tapped, and there is an explicit gap in the literature. First, the applications of AI, ML, and data analytics in the O&G industry are discussed thoroughly, so this paper can be a comprehensive reference for readers and future researchers. Then data preprocessing is explained. This includes data gathering, cleaning, and feature engineering. Next, the different ML models are compared and discussed. Finally, model performance evaluation and best model selection are described. This study would be a concrete foundation in the design and construction of ML programs that can be deployed for WI risk management. The developed model can be simply used for any well stock, providing quick and easy assessment instead of subjective and tedious assessment. The layout can be simply adjusted to reflect the risk profile of any well type or any field.

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A supervised machine learning model to select a cost-effective directional drilling tool

Nour,

Elsayed,

Mahmoud

2024

Sci Rep

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With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.

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Using Artificial Intelligence Techniques in Modeling and Predicting the Rheological Behavior of Nano-Based Drilling Fluids

Cited by 5 publications

References 0 publications

A Comprehensive Review of Nanotechnology Applications in Oil and Gas Well Drilling Operations

A Comprehensive Review of Nanotechnology Applications in Oil and Gas Well Drilling Operations

Addressing Diverse Petroleum Industry Problems Using Machine Learning Techniques: Literary Methodology─Spotlight on Predicting Well Integrity Failures

A supervised machine learning model to select a cost-effective directional drilling tool

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