Integrated Operations Solutions for Upstream Oil and Gas
Saurabh Gupta Solution Architect, Industry Solutions Company - IBM

The article explains Integrated Operations (IO) solutions for Upstream Oil and Gas companies through Information Technology (IT) services to reduce costs by managing their onshore and offshore assets effectively.

Integrated Operations (IO) is very much the cornerstone of any Upstream Oil and Gas company that has assets and resources spread across the globe. These companies look for Information Technology (IT) services to build Integrated Operations solutions to help them reduce costs by managing their onshore and offshore assets effectively.

In this article talks about the IO solution and business value it brings to the Upstream Oil and Gas Companies. In my four years with this Industry, I have observed that a company needs to transform its enterprise IT and business strategy as per the below maturity model to get the best out of the IO solution.

Solution
The IO solution ingests a variety of information gathered from equipment in the field and performs advanced analytics to provide actionable insights for Planning, Surveillance, Production Optimisation, Hydrocarbon Delivery and Equipment Management. These insights can be used in multi-disciplinary, integrated analytical workflows by operations staff across the enterprise .

With this integrated solution, one can:
  1. Monitor and predict the performance and production of reservoirs, wells and facilities
  2. Optimise future production and hydrocarbon delivery
  3. Monitor equipment health, predict failures and other actionable events
  4. Optimise maintenance schedules and repair cycles
  5. Identify the root causes of upstream performance issues and equipment failures
  6. Orchestrate multi-disciplinary workflows using a variety of analytical techniques
These capabilities are critical to fulfill higher level business objectives of an oil & gas operator such as production planning, field surveillance, operational decisions, production optimisation, inventory optimisation and beyond.

Instrumented equipment generates Time Series data such as an asset or equipment ID, temperature and other readings. This data can be collected and used (together with other data such as maintenance schedules) in analytical models that predict field performance, hydrocarbon production and equipment failures. Equipment that can be monitored includes platforms, risers, drills , turbines, pumps, separators, condensers, pipelines, etc. This Time Series data is typically stored in a Data Historian and can be augmented with data from Well Logs (depth dependent data in formats such as WITSML), Maintenance Logs (text), Geospatial data, Video and Image data and so on to develop predictive analytics and optimisation models.

Such models can be used in conjunction with Physics-based modeling and simulation capabilities in analytical workflows that experts from a variety of backgrounds can interact with and use to generate increased value. For example, a Well Engineer using predictive analytics to assist in Well Surveillance may signal a forecasted event, which may in turn be used by a Facilities Engineer to modify the Production outlook or used by a Reservoir Engineer to run a dynamic Reservoir Simulation with the forecasted values as inputs.

The Integrated Operations solution has six main components:
(1) Data Ingestion Layer
This layer is responsible for connecting (via a set of Adapters) to the underlying equipment sensors, Data Historians and other sources of data required for advanced analytics. It is responsible for ingesting all required data into the Data Services Layer.

(2) Data Services Layer
This layer is responsible for the appropriate storage of the ingested data, providing a semantic understanding of the ingested data, ensuring its consistency with Master Data and providing access to the data for advanced analytical processes.

3) Advanced Analytics Layer
This layer is responsible for the development, training, deployment and operational management of advanced analytical models and functions including :
  • Predictive (data-driven) analytics
  • Optimisation and decision management
  • Static and dynamic modeling and simulation (potentially provided by ISV components)
  • Other analytical techniques (potentially provided by ISV component )
(4) Role-based Visualisation Layer
This layer is responsible for providing the relevant solution visualisations to users of the solution. These may include:
  1. Operator and Administrator Consoles
  2. Analytical Dashboards, Reports, Graphs, etc.
  3. Views of the status and execution of Analytical Workflows
(5) Business Process Management Layer
This layer is responsible for the development, deployment and orchestration of multi-disciplinary, analytical workflows or business processes used to perform an end-to-end business function combining instances of the capabilities provided by the layers described above.

(6) Solution Management Layer
This layer is responsible for managing the solution, including:
  1. Providing services for the installation and configuration of the solution's software components
  2. Providing services for the monitoring, logging and auditing of the solution
  3. Providing services for the administration of the solution
Business Values
The business value of the Integrated Operations solution is described in the following list.
  1. Improve Operations
    Business goals include reducing Non-Productive Time (NPT) and downtime and optimising equipment and plant turnaround operations. Globally, non -production time is estimated to be about 15 to 40 per cent which cost USD 14 to USD 37 billion loss in business. The bottom line is that if they are not drilling or producing, they are not making money.
    As an example, for drilling operations - integrate and process multiple real-time data streams from the wellbore with the measurement data generated from the rig. Using streams processing software at the rig and 'scoring' the results from the predictive model built prior to spud allows drilling managers to receive alerts and perform analytics during the drilling process.
  2. Improve Reservoir, Well and Facilities Performance
    Business goals include improving upstream field performance across reservoirs, wells and facilities, managing the integrity of the field and detecting and avoiding leaks and other detrimental scenarios.
  3. Improve Production Optimisation
    The business goal is to maximise the value of hydrocarbon production within the constraints of operations, logistics, supply chain, environmental and other factors.
  4. Improve Forecasting and Allocations
    Business goals include improving forecasting of hydrocarbon production and the ability to allocate that production (by well, for example) to Joint Venture (JV) partners or other allocation requirements.
  5. Improve Maintenance
    Business goals may include optimising logistics and equipment scheduling , predictive maintenance, improving equipment capacity, availability and utilisation, etc to promote the reliability of the equipment (including rotating equipment) under consideration.
  6. Reduce Costs by Deploying Virtual Sensors
    Data-driven analytical models can be used as proxies for physical sensors, gauges and meters. The business goal is to use these 'virtual sensors' to obviate the need to deploy (often expensive) physical sensors, thereby reducing operational costs.
  7. Reduce Costs and Time to Develop and Execute Analytical Models
    Data-driven analytical models can be used in place of physics-based, engineering-based or first principle models, such as dynamic or static simulation models for reservoirs, wells and facilities. Data-driven models can be developed more rapidly (in minutes or hours compared to weeks or months for the other methods noted) and executed more efficiently (in seconds or minutes compared to days or weeks for executing the other models noted). The business goal is thus to use these techniques to reduce the cost and time associated with analytics in Integrated Operations, particularly in cases where scalability of model development and execution is an issue (e.g. in Coal Seam Gas fields with thousands of wells).