Digital Upstream opportunities
Digital Oil & Gas applications are expanding by leaps and bounds that provide insights, improves decision making and execution. Here are top ten areas which could significantly improve business bottom line
Exploration & Production: Increase / Enhance commercial production of hydrocarbon: Artificial Intelligence methodologies could predict realistic values of reserve estimation and production forecasts augmenting the traditional Decline Curve Analysis (DCA). Using Real time data from multiple sources could warn ahead of time of deviations in productions and trigger possible actions.
Group Wells and their Behaviour: Identify group of wells with similar characters / behaviors using artificial intelligence to understand the subsurface, production profiles, pressure, PI etc in managing the wells based on historic and real time data.
Drill Bit Performance: Monitor, identify and predict Bit performance in real time which depends on a number of parameters such as rock strength, flow rate, porosity, permeability and act ahead of time (Proactive)
Well, Facility and Reservoir Management (WRFM): WRFM integrates multiple tools and subject matter experts right from Production, Reservoir management, Petrophysicist, Geologist and much more. This is a fertile area for digital applications right from Real-time data monitoring, exception based surveillance (3 Simga +/- techniques), Daily performance review (24 Hour performance variance/improvements), Reservoir Pressure data etc. ML/ DL/Tensor techniques could be straightaway applied here.
Manage Non-Hydrocarbon Supply Chain efficiently: An interesting and important area – Procurement, Materials Management, Inventory is where millions and billions are spent in Oil & Gas business that lacks transparency, databases that lacks quality and does not communicate across the supply chain impending projects and production. Digital efforts could streamline, improve efficiency on the first phase to subject the Digital supply chain for larger goals such as AI and Blockchain applications.
Which factor really drives? – Variable / Feature Ranking methods: Methods such as PCA could help identify the importance of the order of parameters determining the production performance and help focus efforts on what really drives performance than fishing everywhere. This really helps in reducing cost and time while improving production and profits
AI in In-fill drilling : Determining the number of In-fill wells to be drilled and their location is often a challenge given the limitations of budget and the goal of getting the bang for the bug. AI could help in optimising the number of in fill wells to be drilled based on range of constraints right from Technical, Commercial to financial
AI in Drilling Trajectory / Optimisation: Well Trajectory to be drilled to maximise production while minimising cost and other technical considerations – a real time digital solutions based on sensors, IOT, Big data in real time and AI algos to predict.
Equipment Monitoring (real time monitor of compressors, pumps etc to predict unplanned downtime): This is one of the well known areas of AI and Big data applications in areas such as Pumps, Compressors, Pipelines where historic data and real time data helps to predict unexpected shutdown events leading to non productive time resulting in increased Opex and reduced profit. Artificial Intelligence plays a significant role in predict, proactively act and in some cases even preempt these threats.
AI in Gas-lift wells: Pressure profiles, Productivity Index, sensor datas such as vibrations could help in predicting unpleasant events and take remedial actions on gas-lift adjustments
Enhanced Oil Recovery (EOR): Which assets to invest in EOR and what EOR techniques to use? AI could significantly improve oil recovery from wells based on chemicals / polymers planned and compare with other well behaviour under varied circumstances.
