AI's role at the wellhead 'will be limited'
Artificial Intelligence should be seen as one tool among many, rather than as a panacea for operational inefficiencies at the equipment/well level, according to Grant Eggleton of P2 Energy Solutions. The industry can use artificial intelligence to streamline processes and improve performance, but AI can be time-consuming to train and lack resilience to change, he said. Existing tools, such as metering, well and subsurface models and production accounting, can all be used to create self-service operational intelligence programmes, which would then provide a foundation for AI development, he said.
Production and reservoir engineers are well acquainted with the constant grind of firefighting that defines so much of well management. Effective operation of an asset is an ongoing, complex challenge that requires operators to navigate through an intricate web of process interdependencies to maximize production rates, maintain asset integrity and maximize ultimate recovery.

Until recently, the upstream industry has largely invested in development projects and drilling activities. However, at the current $50/bbl to $55/bbl price, growth through the drillbit can be less attractive. Capital projects have either been terminated in favor of acquisitions and divestitures or put on hold while operational headcount is reduced.

As a result, the pressure is on for engineers to anticipate production issues before they happen, to operate proactive and agile business processes and to have a clear and up-to-date view of the current state of producing assets to make more informed, on-target decisions faster.

But what tools will empower these engineers to get the job done?

In a time when operations are awash in disconnected data and engineers are continually asked to do more with less, emerging technologies like artificial intelligence and machine learning are claimed as a panacea for addressing operational inefficiencies. Artificial intelligence and machine learning can provide brilliant macro learning observation and insights. However, at the equipment/well level, they can be time-consuming to implement and lack resilience to change.

Additionally, there is always effort to build or train the models, a function today that is performed largely by data scientists due to the maturity and complexity of the available tools. Context, experience and tacit knowledge all still count. Even when an artificial intelligence solution is appropriate, the subject matter experts need to be part of the equation.

While artificial intelligence can be a source of information to the engineers, it is only one of many tools available (e.g., metering, well and subsurface models, production accounting, etc.). Rather than adding another gadget into an already-bulging toolkit, engineers need to simplify and consolidate what they do have to achieve something new: operational intelligence.

Operational intelligence

Data are everywhere. SCADA data, production accounting information, drilling and completions information, maintenance and reliability data, well header data, information from spreadsheets—the list goes on and on. Not only is there a ton of data that has a ton of value to any engineering function or optimization activity (the old adage of “you can’t optimize what you don’t measure”), but it also is disjointed in most cases because disparate systems are used to capture it.

Knowing what information to look for and how best to use it become immediate challenges for subject matter experts like production engineers and technicians. That’s why it is important to help engineers be more effective individually and as a team. Providing a centralized reference model of all relevant information relating to an asset gives engineers a rapid and reliable foundation for their workflows.

The industry then needs to provide the tools for interpretation (calculations), diagnostics (analytics) and surveillance (complex event processing and notifications). Adding value here means enabling engineers with self-service capabilities to leverage available data in context and capture their institutional knowledge and subject matter expertise (as opposed to it being stored in a spreadsheet) so that they don’t have to go back to IT to obtain and understand those data.

Shifting the paradigm from ad hoc or reactionary firefighting activities to proactive automated surveillance routines with self-service diagnostics opens up a new world of optimization opportunities.

This allows a single engineer to watch for multiple events customized to the current operating conditions. On notification of an important event, users can view their own diagnostic displays to further investigate the cause of the production issue. All of this allows engineers to understand and interpret data themselves, to see and identify leading indicators to mitigate risk, or reduce its duration in the worst case—and to do it faster.

Artificial intelligence and machine learning are new sources of data, insights and events. All too often the people selling these solutions skip over the need to include the human intelligence that exists in the business.

Here’s where a single data environment and exception- based surveillance processes come in.

A single data environment brings together data from disparate sources and connects it to digital representations of the physical assets, giving production engineers and technicians a one-stop self-service environment for configuring rules and performing key analysis. Exception-based surveillance processes provide a 24/7 watch over company assets and trigger alerts and tasks when predefined events occur, allowing personnel to manage more wells and fix problems in less time.

A practical example: foreign materials. Salt, scale, paraffin and other materials have a tendency to build up in produced water wells. This buildup occurs slowly over time and can hinder production if an acid treatment isn’t mobilized in due course.

This is a scenario where operators could use field data capture information, an injection curve and SCADA data to monitor the pressure-flow correlation against the curve over time.

Using the derived operational intelligence, users could then create a rule that would provide prioritized notifications showing the event in context long before material buildup became a serious issue, giving them plenty of time to plan the intervention activities needed to keep production rates at their highest.

There is still much opportunity to be had in better enabling subject matter experts to optimize production or manage the constraints from the producing assets. Establishing an effective self-service operational intelligence program will deliver immediate benefits and reinvest the knowledge into a foundation that can enable any artificial intelligence program. There is no doubt that the ongoing application of artificial intelligence technologies brings about new perspectives and benefits to production within the oil and gas industry.

There are significant opportunities through self-service operational intelligence to consolidate, contextualize and capture subject matter expertise. By leveraging the connected data, calculations and events within the operational intelligence environment, the artificial intelligence and machine learning solutions will be more comprehensive and on target.

Improved profitability will happen when data from across the enterprise is brought together and presented to the right people and the right systems at the right time. Businesses should consolidate a strategy that better empowers subject matter experts to deliver benefits while establishing the data foundation for an artificial intelligence program.

In addition to the benefits provided by operational intelligence, artificial intelligence tools can adopt that same data foundation investment to identify incremental opportunities and provide insights to engineers and operators.