The oil and gas Industry over the last 5 years struggled with lower oil prices. Oil price dropped from over $100/barrel to about $25/barrel. Prices have averaged $60 — $70 in the last 18 months, however, activities have not returned to what it used to be. The future is also very uncertain. Alternative sources of energy like the renewables is disrupting the market.
The oil and gas industry is beginning to experience the convergence of Industry 4.0 technologies i.e. Internet of Things, Robotics, Big data, Analytics and Additive production systems. The time to leverage these technologies to reduce costs, increase productivity and improve profitability is now.
Data Rich Industry
The oil and gas industry is an asset rich, data big industry. However, less than 3% of data gathered is exploited and used in decision making according to Mckinsey. In the last decade, there has been encouraging participation by the indigenous and local companies. However, these organizations don’t have the tools and capabilities to maximize production. The situation can get better if organizations are able to apply data principles to find solutions to problems. Patterns, trends are identified and statistical relationships defined amongst variables. Cloud computing, inexpensive sensors, progressive network availability are allowing more data to be gathered than ever before. oil and gas IoT applications are able to automate and enhance how valuable insights are communicated.
Asset management remains key in the oil and gas industry. Maintenance programs based on statistical techniques are able to play a key role in forecasting equipment lifecycles and estimating time to failure. Information gathered from enterprise asset management platforms and maintenance logs can be used for detailed analysis. A regression model can be used to predict the remaining life time and failure rate of an asset , while a classification model is used to predict failure within a given time window using known parameters, which contribute to its breakdown.
A proper asset management strategy can help an organization reduce around 30 percent of its operating expenses, implement planned rather than unexpected shutdowns. Predictive analytics enables organizations to improve continuously.
New business models
The word “stand-by” is one which every oil industry professional is familiar with. There are always equipment and personnel on standby in order to avoid down time or perform impromptu operations. With the right data, enterprises are able to predict and forecast the health status of equipment accurately eliminating the need for a “downtime standby”. An option to consider when value is to be paid for is the ‘’charge per use” which leverages the power of accurate data gathering. For instance, sensors can be installed on a crane to measure the amount of load it lifts during its usage in an operation. A vendor is able to capture the amount via a software application. The vendor bills an operator per KG or Tonnes of load lifted. This will create a more competitive procurement process and impact positively on the bottom line.
Inventory planning and logistics is crucial in the oil and gas Industry given that drilling and production operations run 24 hrs. Leveraging data from field operations, maintenance logs, well test and reports, organizations are able to forecast their inventory and logistics needs accurately. When the status of an asset and location is known and data accurately viewed, inventories of spare assets can be reduced.
A digital twin is a virtual replica of an asset, product or process. Digital twin enables oil and gas companies to test a process before it is implemented. For instance, a reservoir can be exposed to different production pressure virtually before a well test operation is carried out. This allows for faster and efficient well test operations. An operator is able to expose the critical components of a wellhead to different situations, this will help reduce the cost associated with wellhead maintenance.
The digital twin concept leverages data. Data is gathered continuously from the asset, product or process of interests to make the right prediction.
Using Data Optimally
Data technologies such as machine learning, analytics and artificial intelligence and IIoT are now being embraced in the oil industry. However, security, safety, Interoperability, people and culture are just some challenges facing the oil and gas industry as it begins to leverage these technologies. Collaboration is key in the data age. Organizations will need to collaborate to share and learn best practices enabling a success transformation for the Nigerian oil and gas Industry.