Evolving market and societal forces are driving today’s energy producers to reimagine their operational business models. The energy sector has recently seen significant shifts and, as a result, business pressures to tackle compounding industry challenges are on the rise.
Energy producers operate in a heavily regulated industry where shifting regulations and incentives add complexity to making informed, long-term investment decisions. Additionally, they’re faced with declining oil prices, which are concurrently driving an increased interest in renewable energy. As oil prices decline and attitudes about fossil fuels evolve, traditional oil and gas producers must identify new ways to improve efficiency, drive down costs, and diversify with environmentally friendly practices.
From targeting new exploration sites and managing people and assets to energy source portfolios, everything is being reevaluated across the three major sectors in the industry: upstream, midstream, and downstream. While energy producers maintain large workforces and billions of dollars’ worth of assets, many still operate processes manually or with limited technology, which lacks the real-time visibility needed to make informed, timely decisions. Successfully navigating this industry inflection point will require smart decisions about where and when to invest in modern, data-driven business approaches.
Embracing Digital Transformation
A new generation of digital tools, technologies, and services are available to help: artificial intelligence (AI) and quantum-inspired optimization services. Energy producers can draw on these innovative capabilities to accelerate profitability and better support their customers.
Technology and services solutions are designed to be a complementary part of business, not just separate entities on their own. They become part of the overarching business plan that will help boost efficiencies, simplify processes and lower costs, protect data integrity, and enhance security to support long-term business goals.
There is a big opportunity for oil and gas companies to explore how technology and services can transform legacy business procedures. It’s important to analyze how technology fits into the overall business and operation, not only how it fits into production elements.
Finding the right partner with the right experience is a critical element in any digital transformation journey, regardless where you may be in the process. Choose partners for their strength and expertise in the area(s) in which you are focusing. Technical or co-creation discovery workshops are invaluable to pinpoint key business challenges and identify where energy producers can reap the most benefit from any new technology or solution.
Working with a partner to assess the business and technical feasibility of implementing technologies like AI or optimization services to help solve key challenges will help create a plan to transform business processes over time. Here are some common business challenges across upstream, midstream, and downstream efforts when planning for digital transformation:
1. Resource Allocation
Resource allocation is an industry-wide challenge and a critical element that impacts business sustainability. Depending on the size of the energy company, it may have thousands of assets, as well as tens of thousands of craftsmen and engineers who constantly must be reassigned in response to changing local conditions. Many energy producers consider cutting staff as a cost-saving alternative, but that’s not always the best route. Oil and gas companies need to evaluate how to optimize resources and important assets to best fit their needs. A few key areas to consider include:
- Staffing allocation: Many energy companies are still manually managing scheduling and allocation of people and teams. Do you have any staffing issues? How can you better manage resources?
- Drilling and completion scheduling: Oil companies typically have more wells available to drill than they have rigs. Where, and in which order, should those rigs be deployed?
- Transport channel optimization: Energy producers employ a variety of transportation modes to transport huge volumes of fuel every day. Which mix offers the safest and most cost-effective transport today? Next month? Three months from now?
- Turnaround optimization: Shutting down production on plants and facilities for corrective or preventive maintenance can be expensive. What is the optimal schedule to minimize costs and downtime?
With so many varying needs, this is where technology can lend a powerful hand. Quantum-inspired technology and services can calculate combinatorial optimization problems related to resource allocation by identifying the quickest, most cost-effective allocation and logistics plan beyond what today’s conventional computing systems can provide.
Energy companies can calculate – continually, in real time – the optimal schedule and logistics for employee assignments, asset allocation, drilling and completion scheduling, and more. As a result, they can substantially reduce costs and delays, and generate new revenues from resources that would otherwise sit idle. At the same time, they can take better care of their people, more easily incorporating employees’ preferences and work/life balance into scheduling.
2. Portfolio Asset Optimization
Managing billions of dollars in energy assets is extremely complex given the massive amount of data, variables, and constraints. Energy producers need to make informed business decisions that align with short and long-term investment strategies based on highly lucrative oil and gas assets while generating the greatest return on investment.
Producers need to leverage technology, such as quantum-inspired optimization services, that offers hundreds, even thousands, of times the speed and accuracy compared to the current state of classical computing systems. This enables the ability to navigate energy market volatility, reduce risk, and transform complex business processes.
This type of technology allows for faster optimization, which means more frequency in rebalancing of portfolios, improving returns, and reducing risk. A greater optimization scope enables better decisions to be made to improve portfolio performance.
3. Infrastructure and Asset Inspection
Scanning assets and infrastructure can be time consuming, requiring days based on manual screening processes. For producers of both fossil fuels and renewable sources, many aspects of day-to-day operations are still largely manual.
That’s especially true when it comes to inspecting infrastructure and assets, such as wind turbines or pipeline welds. These efforts are critical to protect assets, workers and communities, and avoid potentially catastrophic environmental disasters.
But relying solely on human evaluation carries longer timelines and higher costs. With new advances in nondestructive testing (NDT) and AI-based image recognition, energy companies can significantly reduce maintenance costs. Through the use of automated analysis of 2D/3D images, sensor data (vibration, ultrasound) and X-rays, defects can quickly and reliably be identified. AI recognition models can be adapted as changing requirements arise.
4. Storage Optimization
Midstream companies need to meet contractual obligations of clients by storing different types and certain amounts of products. Several storage options exist for midstream companies, including storage that can only hold one type of product, and underground storage that can store more than one type of compatible products. In addition to this, it is possible to transfer products from one storage area to another and at a certain transfer cost, as well as potential cleaning costs.
The complexity of routing and storage of oil and gas from upstream to downstream, through pipeline, rail, trucks and reservoirs, all while trying to manage costs and maximize profits, can get cumbersome. Storage challenges can be addressed through real-time scheduling using quantum-inspired optimization services to effectively maximize revenues, with the potential to improve cost savings upward of 25 percent.
5. Refinery Production Scheduling (RPS)
RPS is all about understanding, modeling, and solving production problems inside a very complex environment – one that must account for the management of timing, sizing, allocation, and sequencing decisions in a connected and nonlinear world. Refineries must balance optimization trade-offs, processing models, and complex blending in parallel with many technical, economic, environmental, and commercial constraints.
RPS can become complicated and costly from a time and resource perspective when only tackled manually. Quantum-inspired optimization services can drastically improve scheduling efficiency through the use of algorithms designed to account for all critical factors necessary for scheduling. They will assess the combinatorial optimization problems and will rapidly calculate the most efficient scheduling solution. This can result in large cost savings and improved oil feeds.
The time to embrace digital transformation is now, especially as we navigate a new normal during and after COVID-19. By harnessing new digital investments in people, training, and technology, energy producers can reimagine their operational models, which will pay dividends and benefit ROI well into the future, moving their businesses and balance sheets toward more sustainable footing.
Headline photo courtesy of iStock
Oil and gas companies are regularly faced with many industry-specific issues to overcome. Such issues, including exploration and drilling, are often complex and intricate processes with many unique challenges to overcome. Data analytics can play a massive part in streamlining some of the most fundamental operations that are involved in the oil and gas industry.