oilgasdigitalshift’s Post

Discover how Machine Learning is transforming Oil & Gas! Dive into Part 1 of my new series, where we explore practical applications in engineering and construction management. Read more:

“We are a long way from that of course but the advances in large language models have the ability to make our industry more intuitive, which will in turn increase efficiency of our operations,” Knott added. Check out our CEO, Vicki Knott, P.Eng's article with Oil & Gas Jobs by Rigzone about Machine Learning and AI Adoption:

Even though digital twins are already a complex topic (according to the specific type and, of course, specific process), we still had to add AI into the mix 🤖   There’s this concept of cognitive digital twins, which are advanced representations of physical objects or systems but equipped with AI capabilities.   To put it simply, traditional digital twins mimic real-world behavior using real-time data. While cognitive digital twins analyze and learn from the data to adapt to complex problems in real-time.   However, these two concepts still share two very important challenges:   The reliability of quality data.   And…   The consumption of resources.   Cognitive digital twins also need accurate data to make sure they can operate effectively. And using AI means using machine learning, deep learning, and natural language processing which are complex and resource-intensive.   If this looks like too much already, you can learn the basics of digital twins in our latest article here 😉 https://lnkd.in/dQVx3Fbr #DigitalTwins #CognitiveTwins #AI #OperationalTechnology #OpTech #CSE #CSEICON

Low-cost machine learning framework for snail trail detection in PV panels #ML #MachineLearning #Energy #GreenTech #Power #CleanEnergy #EnergyTransition #Renewables #RenewableEnergy #GreenEnergy #SolarEnergy #SolarPower #SolarPV #SolarPanels #SolarModules #SolarTech #Photovoltaics #PVSystems via pv magazine Global

Over the past few weeks, I have delved into several advanced topics that are shaping the future of the oil and gas sector. Here’s a brief overview of what I’ve learned by this course from Larsen & Toubro popularly known as L&T: Advances in AI Technology for O&G Industry: Understanding how artificial intelligence is transforming operations and decision-making processes. Data Handling in O&G Industry: Exploring best practices for managing vast amounts of data generated in oil and gas operations. Onshore and Offshore Hydrocarbon Storage Facilities: Gaining insights into the design and management of storage solutions for hydrocarbons. Seismic Data Processing using AI & ML: Learning how machine learning enhances the interpretation of seismic data for better resource exploration. Overview of Crude Oil Treating Systems: Understanding the processes involved in treating crude oil to meet market specifications. Geomodelling Process: Exploring the techniques used to create accurate geological models that aid in reservoir management. ML in Reservoir Engineering: Investigating the application of machine learning techniques to optimize reservoir performance. Overview of Natural Gas Production and Processing: Gaining knowledge about the methods used in natural gas extraction and processing. Overview of Oil and Gas Exploration and Drilling Methods: Learning about the latest techniques and technologies used in exploration and drilling. I am excited to apply this knowledge to real-world challenges in the industry and contribute to innovative solutions moving forward!#AdvancesInAITechnology #OilAndGasIndustry #DataHandling #OnshoreAndOffshoreStorage #HydrocarbonStorage #SeismicDataProcessing #AIandML #CrudeOilTreating #GeomodellingProcess #MachineLearningInReservoirEngineering #NaturalGasProduction #OilAndGasExploration #DrillingMethods

RAGAS is a quicker and cheaper alternative to using LLMs for evaluating the RAG pipeline's performance. In the second article of the Superlinked series, Atita Arora goes in-depth on how to set up your #RAGAS evaluation framework. Learn how to: ✅ Create an evaluation dataset that serves as a reference point for evaluating our RAG pipeline. ✅ Understand the evaluation metrics and how to make sense of them. ✅ Test a basic RAG pipeline and measure its performance using RAGAS metrics. This one goes deep, so get ready to dive in and enjoy the read!

In the oil and gas industry, the perceived value of machine learning is undoubtedly high, but its application poses significant challenges. Here are four use cases for implementing machine learning and data analytics in order to benefit oil and gas production, and how you can expect a significant ROI --> https://lnkd.in/eJRJ2Y8M #oilandgas #machinelearning #production #dataanalytics

✈️On my way back from Copenhagen to Stavanger after an existing day with amazing people - topic: AI for integrated O&G asset modeling! ✈️ The topic of today was how generative AI be used in oil and gas companies for “integrated asset modeling” to evaluate, compare and optimize field development planning scenarios. Integrated asset modeling involves combining input data from many sources such as oil production potential profiles, subsurface data, well constraints, economic data (capex, open, apex), facility data, rig scheduling, fiscal regimes etc. Scenario realizations can be run stochastically and deterministically - and evaluation relates to eg oil production, economics, risk, IRR, profitability etc. Generative AI can be used for integrated asset modeling qnd related use cases when the right tools and used the right way using the right data - and WHEN combined with knowledge about oil and gas production systems and networks. Generative AI indeed possess significant opportunities! - now with the latest OpenAI o1 model being released (out of preview). It is extremely powerful and outcompetes human experts on different of benchmarks - give it a try! Greetings from CPH 😀

Retrieval-Augmented Generation(RAG) Evaluation Metrics: Evaluating RAG System Involves Assessing both The Retrieval and generation Component ability to Produce relevant and accurate answers. Here's Breakdown of key RAG Evaluation metrics Using RAGAs(Retrieval-Augmented Generation Assessment) To Evaluate RAG System RAGAs Expects Following Information. 1. Question : Input to the RAG System i.e user query 2. Answer : Answer Generated by RAG System 3. Context : Context Retrieved from External data source 4. Ground-Truth : The Correct answer to the question this is Human labelled information. - Retrieval Component 1. Context-Precision : Measures How relevant are the retrieved contexts to the given question. this metric is computed using question and context. Context-Precision@k = Precision@k / total number of relevant Documents in top-k result precision@k = true positives@k / (true positives@k + false positives@k) k = total number of chunks in context 2. Context-Recall : measures if the retriever able to retrieve all the context relevant to ground truth answer. metric is computed using context and ground truth Context-recall = Number of relevant documents in top-k / total number of relevant documents - Generation Component: 1. Answer Relevancy: Measures how relevant is our generated answer to the input question. metric is computed using Question and Answer. for example the answer "The France is in west Europe" to the question "where is France and what is it's capital" achieve low relevance. because answer contains half information. 2. Faithfulness: Measures how factually accurate the generated answer relevant to retrieved context. It tries to address the problem of Hallucination. metric is computed using generated answer and context. faithfulness = number of correct statements in context / Total number of correct statements in generated answer All metrics values lies in the range of [0,1] higher the value better is the performance. All the above metrics can be computed using RAGAs More about RAGAs: https://lnkd.in/gJqzVvKM Checkout my other posts on ML topics: ML In Production : https://lnkd.in/geR9F-mV SQL Injection Detection : https://lnkd.in/grUAe-Eq NLP : https://lnkd.in/gbyujk3F

In Part 1 of this three-part series, we use long short-term memory (LSTM), a machine learning technique, to predict #oil, #gas, and water production using real field data. #machinelearning https://lnkd.in/dcv8-JP9