Ai In Oil And Gas | AI Final Year Project Report

Complete Technical Paper Content — Ai In Oil And Gas

1. Executive Abstract, Problem Formulation & Background

The AI boom is a period of rapid growth in the field of artificial intelligence beginning in the mid-2010s and accelerating in the 2020s. Generative AI technologies include large language models (LLM), which generate text and code, image models, and video models, and world models. This period is sometimes referred to as an AI spring, a term used to contrast it with previous AI winters. Key advances preceding the boom include the transformer (2017), generative pre-trained transformer (2018) and reinforcement learning from human feedback. During the boom, LLMs have evolved via the reasoning model, mixture of experts technique, and multimodal learning. As of 2026, leading proprietary LLMs have been released by Anthropic, OpenAI, and divisions of Google, Meta, and SpaceX in the United States. In China, companies favour open weights releases, led by Alibaba Cloud, DeepSeek, Moonshot AI, and Z.ai. Controversies of the AI boom include its environmental impact, dominance by US Big Tech companies, effect on copyright, proliferation of deepfakes, threats to cybersecurity and biosecurity, its use in warfare, a potential economic bubble, and existential risks such as AI takeover resulting from artificial general intelligence and superintelligence. Since 2018, observers including Ian Bremmer, Nicholas Thompson, and Kai-Fu Lee have argued an AI arms race or AI Cold War exists between the US and China. The boom has impacted the semiconductor industry, causing a global memory supply shortage; As of second quarter 2026, Nvidia is the world's largest corporation by capitalization while TSMC, Broadcom, and Samsung are among the ten largest. ChatGPT is the fifth-most-visited website globally.

In 1950, Alan Turing proposed the idea of "Thinking Machines". These were computers that would be able to reason at the same level as humans. He began his well-known "Turing test", where an interrogator is provided with two materials and they must determine which one was done by artificial intelligence and which one was done by a human being.

In 1956, John McCarthy used the term "artificial intelligence" for the first time. That year, McCarthy, Nathaniel Rochester, Marvin Minsky, and Claude Shannon organized the Dartmouth conference, which formalized artificial intelligence as an academic field. In 1958, McCarthy created the programming language LISP, LISP stands for "List Processing" and was the main programming language for artificial intelligence. which remained the most common programming language for artificial intelligence in the United States for decades. In 1962, McCarthy founded Stanford Artificial Intelligence Laboratory (SAIL). McCarthy was also a cofounder of MIT's first Artificial Intelligence Laboratory, now known as MIT Computer Science and Artificial Intelligence Laboratory. In 1966, Joseph Weizenbaum created ELIZA, the first chatbot, as an experimental emotional tool. Between 1974 and the early 1990s period, an "AI winter" took place. This term, was first used in 1984 and defined the gap between the AI expectations and the technology’s shortcomings. AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. By 2000, solutions developed by AI researchers were widely used. Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field. Neural-network approaches also advanced natural language processing. In 2013, word2vec introduced efficient methods for learning distributed word representations. Beginning in the mid-2010s, generative AI systems began to rapidly improve. In 2014–2015, sequence-to-sequence and attention mechanisms advanced neural machine translation. In 2014, the introduction of models such as the variational autoencoder (VAE) and generative adversarial network (GAN) enabled effective deep generative modeling of complex data such as images. In 2017, researchers at Google Brain introduced the transformer architecture in a paper titled "Attention Is All You Need". The transformer is based on self-attention, which later became the basis of many large language models. Large language models are trained on vast quantities of data and can be adapted to a wide range tasks. They can discuss a huge number of topics and display general knowledge. Investment in AI grew exponentially after 2020, with venture capital funding for generative AI companies increasing dramatically. Total AI investments rose from $18 billion in 2014 to $119 billion in 2021, with generative AI accounting for approximately 30% of investments by 2023. The commercial AI scene became dominated by American Big Tech companies, whose investments in this area surpassed those from U.S.-based venture capitalists. OpenAI's valuation reached $86 billion by early 2024, while NVIDIA's market capitalization surpassed $3.3 trillion by mid-2024, making it the world's largest company by market capitalization as the demand for AI-capable GPUs surged.

GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. The text-to-image models DALL-E 2 and Midjourney were released in 2022. ChatGPT, an AI chatbot created by OpenAI, launched in November 2022 and became the fastest-growing consumer software application in history, gaining over 100 million users in two months. The success of ChatGPT brought generative AI into the public consciousness, popularizing the use of generative AI for general-purpose text-based tasks.

As time passed, the power of generative AI grew stronger. In 2015, initial popularity began to grow with the release of Google's DeepDream. DeepDream is a generative AI that takes inputs from a previous image and morphs them to produce hallucinogenic images. In January 2021, OpenAI released DALL-E, allowing for image generation through text prompts. This allows users to generate any image with a simple prompt. Soon after, other powerful models followed DALL-E, such as Google's Gemini. The popularity of text-to-video generative AI tools grew exponentially. With the release of models such as OpenAI's Sora in 2024, the use of text-to-video tools became normalized, as people used them for advertisements, which saves on production costs and increases production speed. Generative AI is growing at a rapid rate, outpacing modern-day detection tools. With the common public having access to these tools, it raises concerns about the ethical use of generative AI. There have been multiple occasions where misinformation has been spread over the internet about politics due to a generated or deep-faked video, posing as a security threat.

Generative coding can be used to produce, edit, explain, and debug code. A 2026 study in the journal Management Science found that less experienced developers have higher adoption rates and greater productivity gains.

2. Related Work, Literature Survey & Algorithmic Foundations

The use of AI leads to a request and the needs of more energy. To meet this enormous demand, engineering realities show that the U.S. gas production will increase by 10 to 20% by 2030, as most of the additional electricity generation will mostly come from burning natural gas. Then, after 2030, those demands will probably be met by additional solar capacity and nuclear energy Electricity consumed by hardware used for AI has increased demands on power grids, which has led to prolonged use of fossil fuel power plants which would otherwise have been deactivated, since renewable energy sources often lack the continuous baseload power required for 24/7 AI model training and inference. Microsoft, Google, and Amazon have all invested in existing or proposed nuclear power plants to meet these demands. In September 2024, Microsoft signed a deal with Constellation Energy to purchase power from a reactor at Three Mile Island which had been shut down in 2019. The reactor is set to reopen in 2028 to provide power to Microsoft's data centers. The reactor is next to the unit which caused the worst nuclear power accident in US history in 1979.

Some economists have been optimistic about the potential of the current wave of AI to boost productivity and economic growth. Notably, Stanford University economist Erik Brynjolfsson, in a series of articles has argued for an "AI-powered Productivity Boom" and a "Coming Productivity Boom". At the same time, others like Northwestern University economist Robert Gordon remain more pessimistic. Brynjolfsson and Gordon have made a formal bet, registered at long bets, about the rate of productivity growth in the 2020s, to be resolved at the end of the decade. Big Tech companies view the AI boom as both opportunity and threat; Alphabet's Google, for example, realized that ChatGPT could be an innovator's dilemma-like replacement for Google Search. The company merged DeepMind and Google Brain, a rival internal unit, to accelerate its AI research. The market capitalization of Nvidia, whose GPUs are in high demand to train and use generative AI models, rose to over US$3.3 trillion, making it the world's largest company by market capitalization as of 19 June 2024 and became the first company to reach US$4 trillion on 9 July 2025 and subsequently US$5 trillion on 29 October later that same year. In 2023, San Francisco's population increased for the first time in years, with the boom cited as a contributing factor. Machine learning resources, hardware or software can be bought and licensed off-the-shelf or as cloud platform services. This enables wide and publicly available uses, spreading AI skills. Over half of businesses consider AI to be a top organizational priority and to be the most crucial technological advancement in many decades. Across industries, generative AI tools are becoming widely available through the AI boom and are increasingly used in businesses across regions. A main area of use is data analytics. Seen as an incremental change, machine learning improves industry performance. Businesses report AI to be most useful in increased process efficiency, improved decision-making and strengthening of existing services and products. Through adoption, AI has already positively influenced revenue generation in multiple business functions. Businesses have experienced revenue increases of up to 16%, mainly in manufacturing, risk management and research and development. AI and generative AI investments have been increasing with the boom, increasing from $18 billion in 2014 to $119 billion in 2021. Most notably, the share of generative AI investments was around 30% in 2023. Further, generative AI businesses have seen considerable venture capital investments even though regulatory and economic outlooks remain in question. Tech giants capture the bulk of the monetary gains from AI and act as major suppliers to or customers of private users and other businesses. With the introduction of AI, there has been an exponential rise in production for businesses. It's expected that workers could use resources provided by artificial intelligence to boost their productivity. As many small businesses don't use AI, it's believed that if it's adopted by more businesses, the whole work structure could be changed, as many tasks will be automated by AI. The U.S. Census Bureau's Business Trends and Outlook Survey measured AI use at 3–9% of businesses in March 2025, using language that asked whether firms used AI "to produce goods and services". After revising the question to cover "any business function", the Bureau's measured adoption rate increased to 18% in March 2026. According to Bell & Korinek (2023), the economic effect of AI could worsen economic inequality, which could potentially threaten democracy in ways which are separate from threats caused by AI's use in misinformation and propaganda. As of second quarter 2026, Nvidia is the world's largest corporation by capitalization, and semiconductor manufacturers TSMC, Broadcom, and Samsung are among the ten largest.

Inaccuracy, cybersecurity and intellectual property infringement are considered to be the main risks associated with the boom, although not many actively attempt to mitigate the risk. Large language models have been criticized for reproducing biases inherited from their training data, including discriminatory biases related to ethnicity or gender. As a dual-use technology, AI carries risks of misuse by malicious actors. As AI becomes more sophisticated, it may eventually become cheaper and more efficient than human workers, which could cause technological unemployment and a transition period of economic turmoil. Reaction to the AI boom has been mixed, with some hailing the new possibilities that AI creates, its sophistication and potential for benefiting humanity; while others denounced it for threatening job security and for giving 'uncanny' or flawed responses. Some observers argue an AI arms race or AI Cold War exists between the US and China. The term "AI Cold War" was coined in 2018 by Ian Bremmer and Nicholas Thompson in a Wired magazine article. Kai-Fu Lee, the president of Google China from 2005 to 2009, made a similar claim in his 2018 book AI Superpowers, further arguing "If data is the new oil, then China is the new Saudi Arabia."

Tech companies such as Meta, OpenAI and Nvidia have been sued by artists, writers, journalists, and software developers for using their work to train AI models. Early generative AI chatbots, such as the GPT-1, used the BookCorpus, and books are still the best source of training data for producing high-quality language models. ChatGPT aroused suspicion that its sources included libraries of pirated content after the chatbot produced detailed summaries of every part of Sarah Silverman's The Bedwetter and verbatim excerpts of paywalled content from The New York Times. In protest of the UK government holding consultations on how copyrighted music can legally be used to train AI models, more than a thousand British musicians released an album with no sounds, entitled Is This What We Want?

The ability to generate convincing, personalized messages as well as realistic images may facilitate large-scale misinformation, manipulation, and propaganda. On 19 April 2024, as part of an ongoing feud with fellow rapper Kendrick Lamar, the artist Drake released the diss track "Taylor Made Freestyle", which featured AI-generated vocals imitating the voices of Tupac Shakur and Snoop Dogg. Shakur's estate threatened to sue over the use of Shakur's likeness, saying that it constituted a violation of Shakur's personality rights. On 20 May 2024, following the release of a demo of updates to OpenAI's ChatGPT Voice Mode feature a week earlier, actor Scarlett Johansson issued a statement in relation to the "Sky" voice shown in the demo, accusing OpenAI of producing it to be very similar to her own, and her portrayal of the artificial intelligence voice assistant Samantha in the film Her (2013), despite Johansson refusing an earlier offer from the company to provide her voice for the system. The agent of the unnamed voice actress who voiced Sky stated that she had recorded her lines in her natural speaking voice and that OpenAI had not mentioned the movie Her nor Johansson. Several incidents involving the sharing of non-consensual deepfake pornography have occurred. In late January 2024, deepfake images of American musician Taylor Swift proliferated. Several experts have warned that deepfake pornography is more quickly created and disseminated, due to the relative ease of using the technology. Canada introduced federal legislation targeting sharing of non-consensual sexually explicit AI-generated photos; most provinces already had such laws. In the United States, the DEFIANCE Act was introduced in March 2024.

A large amount of electricity is needed to power generative AI products, making it more difficult for companies to achieve net zero emissions. From 2019 to 2024, Google's greenhouse gas emissions increased by nearly 50%, partly as a result of increased energy consumption by AI data centres.

3. Dataset Ingestion, Preprocessing & Stochastic Augmentations

Industry leaders and others have signed the Statement on AI Risk, arguing that humanity might irreversibly lose control over a sufficiently advanced artificial general intelligence (AGI).

Much of the AI boom has been funded by loans and venture capital, but many commercial AI services remain of questionable practical utility or quality for business. Despite more than $60 billion in corporate investment in AI in 2025, 95% of business AI projects are unprofitable, according to research from MIT. Producers of generative AI, such as OpenAI, also themselves currently have costs greatly exceeding their revenue. As other major tech companies such as Nvidia are both heavily invested into AI and dependent on the AI ecosystem and its hardware demands for their own ongoing growth, this has raised speculation of a wider economic bubble in the tech industry, particularly if future demand falls short of the current levels of AI investment.

AI bubble AI datacenter AI effect AI slop AI winter, a period of reduced funding and interest in artificial intelligence research History of artificial intelligence History of artificial neural networks Hype cycle List of artificial intelligence projects List of artificial intelligence journals Lists of open-source artificial intelligence software Progress in artificial intelligence Regulation of artificial intelligence Technological singularity Timeline of artificial intelligence risks in global finance

Artificial intelligence (AI) and its subfields have been used in applications throughout industry and academia. Machine learning has been used for various scientific and commercial purposes, including language translation, image recognition, decision-making, credit scoring, and e-commerce. Since the 2020s, massive advancements have been made in the field of generative AI (GenAI) as a result of the 2017 inception of Transformer Model architecture. GenAI has the ability to create text, images, voice, music, videos, and other forms of data from chat-based prompting.

In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation.

A 2019 study found that generative AI increased productivity by 15% in contact centers. Another 2019 study found it increased productivity by up to 40% for writing tasks. An August 2025 review by MIT found that of surveyed companies, 95% did not report any improvement in revenue from the use of AI. In September 2025, a Harvard Business Review article described how increased use of AI does not automatically lead to increases in revenue or actual productivity. Referring to "AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task", the article coined the term workslop. Per studies done in collaboration with the Stanford Social Media Lab, workslop does not improve productivity and undermines trust and collaboration among colleagues. In telehealth, agentic AI is reportedly facilitating the creation of large business models (millions in annual profit) with 1–2 employees, such as MEDVi, which as of August 2025 only had 2 employees and ~$75M in annual profit for GLP-1 weight-loss telehealth services.

4. Neural Network Backbone, Layer Specifications & Loss Formulations

Research and development of quantum computers has been performed with machine learning algorithms. For example, there is a prototype, photonic, quantum memristive device for neuromorphic computers (NC)/artificial neural networks and NC-using quantum materials with some variety of potential neuromorphic computing-related applications. The use of quantum machine learning for quantum simulators has been proposed for solving physics and chemistry problems.

Artificial intelligence and machine learning are used in electronic design automation to assist with integrated circuit design, including floorplanning, placement, routing, functional verification, and optimization of power, performance, and chip area.

Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid application development environments The linked list data structure Automatic storage management Symbolic programming Functional programming Dynamic programming Object-oriented programming Optical character recognition Constraint satisfaction

AI programs have been used in hiring processes to screen resumes and rank candidates based on their qualifications, predict a candidate's likelihood of success in a given role, and automate repetitive communication tasks using chatbots. Studies on these programs have identified tendencies for gender bias, favoring male names and male-coded characteristics, as well as bias against disabled candidates and racial minorities.

AI underlies avatars (automated online assistants) on web pages. It can reduce operation and training costs. In 2016, Pypestream automated customer service for its mobile application to streamline communication with customers. A Google app analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. Amazon uses a chatbot for customer service that can perform tasks like checking the status of an order, cancelling orders, offering refunds, and connecting the customer with a human representative.

In educational institutions, AI has been used to automate routine tasks such as attendance tracking, grading, and marking. AI tools have also been used to monitor student progress and analyze learning behaviors, with the goal of facilitating timely interventions for students facing academic challenges. As of 2025 and 2026, respectively, about 80% of high school students and nearly 60% of college students were also using AI. Some schools and universities proposed restrictions on students' usage due to issues with increased cheating, over-reliance on AI tools, and widening of digital inequities.

5. Training Protocol, Convergence Behavior & Experimental Evaluation

The U.S. Department of Energy wrote in an April 2024 report that AI may have applications in modeling power grids, reviewing federal permits with large language models (LLMs), predicting levels of renewable energy production, and improving the planning process for electrical vehicle charging networks. Other studies have suggested that machine learning can be used for energy consumption prediction and scheduling (e.g., to help with renewable energy intermittency management).

The late 1980s and early 1990s saw expert systems used in technical and environmental domains. For example, researchers built a fishway design advisor to recommend fish passage structures under varying hydraulic and biological conditions using the VP-Expert shell. Transportation researchers applied the same shell to balance airport capacity with noise-mitigation plans. In agriculture, a potato insect expert system (PIES) supported pest management decisions for Colorado potato beetle. The U.S. Environmental Protection Agency's CORMIX system for modeling pollutant discharges combined rules with Fortran hydrodynamic models. Autonomous ships that monitor the ocean, AI-driven satellite data analysis, passive acoustics or remote sensing and other applications of environmental monitoring make use of machine learning. For example, "Global Plastic Watch" is an AI-based satellite-monitoring platform for analysis/tracking of plastic waste sites to help with the prevention of plastic pollution—primarily ocean pollution—by helping identify who mismanages plastic waste and where dumping into oceans occurs.

AI applications analyze media content such as movies, TV programs, advertisement videos, and user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or facial recognition techniques, or the analysis of video for recognizing scenes, objects, or faces. AI-based media analysis can facilitate media search, the creation of descriptive keywords for content, content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products, or celebrity faces for ad placement.

Motion interpolation Pixel-art scaling algorithms Image scaling Image restoration Photo colorization Film restoration and video upscaling Photo tagging Text-to-image models (such as DALL-E, Midjourney, and Stable Diffusion) Image to video Text to video (such as Make-A-Video from Meta, Imagen video and Phenaki from Google) Text to music with AI models (such as MusicLM) Text to speech (such as ElevenLabs and 15.ai) Motion capture

AI has been used to compose music of various genres. David Cope created an AI called Emily Howell that became well known in the field of algorithmic computer music. The algorithm behind Emily Howell is registered as a US patent. In 2012, AI Iamus created the first complete classical album. AIVA (Artificial Intelligence Virtual Artist), composes symphonic music, mainly classical music for film scores. It became the first virtual composer to be recognized by a musical professional association. Melomics creates computer-generated music for stress and pain relief. The Watson Beat uses reinforcement learning and deep belief networks to compose music on a simple seed input melody and a select style. The software was open sourced, and musicians such as Taryn Southern collaborated with the project. South Korean singer Hayeon's debut song, "Eyes on You", was composed using AI which was supervised by real composers, including NUVO.

Narrative Science sells computer-generated news and reports. It summarizes sporting events based on statistical data from a game. It also creates financial reports and real estate analyses. Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football. Yseop uses AI to turn structured data into natural language comments and recommendations. It writes financial reports, executive summaries, and personalized sales or marketing documents in multiple languages, including English, Spanish, French, and German. TALESPIN made up stories similar to the fables of Aesop. The program started with a set of characters who wanted to achieve certain goals. Mark Riedl and Vadim Bulitko asserted that the essence of storytelling was experience management, or "how to balance the need for a coherent story progression with user agency, which is often at odds". While AI storytelling focuses on story generation (character and plot), story communication also received attention. In 2002, researchers developed an architectural framework for narrative prose generation. They faithfully reproduced text variety and complexity on stories such as Little Red Riding Hood. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize. South Korean company Hanteo Global uses a journalism bot to write articles. Literary authors are also exploring uses of AI. An example is David Jhave Johnston's work ReRites (2017–2019), where the poet created a daily rite of editing the poetic output of a neural network to create a series of performances and publications.