Nanoparticle Applications as Beneficial Oil and Gas Drilling Fluid Additives: A Review

Machine learning in subsurface geothermal energy: Two decades in review

Determining formation pressure in the well extraction zones is a key task in monitoring the development of hydrocarbon fields. Direct measurements of formation pressure require prolonged well shutdowns, resulting in underproduction and the possibility of technical problems with the subsequent start-up of wells. The impossibility of simultaneous shutdown of all wells of the pool makes it difficult to assess the real energy state of the deposit. This article presents research aimed at developing an indirect method for determining the formation pressure without shutting down the wells for investigation, which enables to determine its value at any time. As a mathematical basis, two artificial intelligence methods are used – multidimensional regression analysis and a neural network. The technique based on the construction of multiple regression equations shows sufficient performance, but high sensitivity to the input data. This technique enables to study the process of formation pressure establishment during different periods of deposit development. Its application is expedient in case of regular actual determinations of indicators used as input data. The technique based on the artificial neural network enables to reliably determine formation pressure even with a minimal set of input data and is implemented as a specially designed software product. The relevant task of continuing the research is to evaluate promising prognostic features of artificial intelligence methods for assessing the energy state of deposits in hydrocarbon extraction zones.

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Predicting dynamic formation pressure using artificial intelligence methods

The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012–2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored.

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A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

Due to the high enthalpy of fusion in water, ice storage systems are known as one of the best cold thermal energy storage systems. The phase change material used in these systems is water, thus it is inexpensive, accessible, and completely eco-friendly. However, despite the numerous advantages of these systems, the phase change process in them is time-consuming and this leads to difficulties in their practical application. To solve this problem, the addition of nanomaterials can be helpful. This study aims to investigate the compound heat transfer enhancement of a cylindrical-shaped unit equipped with double helically coiled coolant tubes using connecting plates and nano additives as heat transfer augmentation methods. Complex three-dimensional numerical simulations are carried out here to assess the best heat exchanger material as well as the impact of various nanoparticle types, including alumina, copper oxide, and titania, and their concentrations in the PCM side of the ice storage unit. The influence of these parameters is discussed on the charging rate and the temperature evolution factor in these systems. The results suggest that using nano additives, as well as the connecting plates, together is a promising way to enhance the solidification rate by up to 29.9%.

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Compound Heat Transfer Augmentation of a Shell-and-Coil Ice Storage Unit with Metal-Oxide Nano Additives and Connecting Plates

Application of machine learning and artificial intelligence in oil and gas industry

Applications of nanoparticles in enhanced oil recovery

Modeling reserves of geothermal reservoirs in Gujarat and Maharashtra in India are in nascent stage. Prognosis of reserve estimate for these shallow prospects is attempted for the first time. Genetic-based Monte Carlo Modeling was used to carry out the systematic investigation. Input parameters required for the model are area of the thermal reservoir, specific heat of the rock, specific heat of reservoir water and average annual surface temperature. Range of values for each parameter were collected using geophysical and logging surveys. Gravity and Magnetic surveys were utilized to have a range of values for prospect closure area, porosity logs were utilized for porosity ranges for reservoirs. Temperature probes were carried out in 19 locations, 17 in Gujarat and 2 in Maharashtra. Specific heat of water was collected from the reports of abandoned hydrocarbon wells. The distribution of values for each parameter was found to be triangular or log normal. The modeling algorithm then defined a relationship between input and output parameters. Genetic-based steps such as random population creation, encoding, crossover, mutations and decoding were performed to assign probability to likely outcome of reserve. The last step of the model involved analyzing the results of the calculation and providing a probability distribution function for each result. Heat capacities of places like Shirdi, Saputara and Harsani were found to be highly encouraging.

Kamakshi Rayavarapu

Papers

Application of machine learning and artificial intelligence in oil and gas industry

Applications of nanoparticles in enhanced oil recovery

Genetic-based Monte Carlo Modeling of geothermal prospects: Indian examples