Internet of Things in the Oil Industry

Information Systems Summative

Literature Review

Internet of Things (IoT) places a focus on recombining new innovations with old ideas where the process of replaces manual tasks leading to efficiency gains.  High levels of connectedness stimulate cost-saving prospects and performance on a global level. In regards to competitiveness, IoT is becoming a differentiating factor where companies are capturing the full range of benefits. New value is created, reliability is improved and operations are optimized from the deployment of IoT. Information received creates value that must be captured by companies within the industry. Business Process Re-engineering involves re-designing existing processes in order to reduce costs and improve quality. Systems that deliver the most business value must be identified while the risks must be assessed. The design process must be organised to ensure the system reflects information requirements and business priorities (Burlton, 2011). The system must be tested for functionality and maintained to correct possible errors. The impact must be measured in terms of the changes in processes linked to business performance indicators.

 The Oil industry is open to unpredictability; new ideas must be developed in order to improve operations to meet various conditions within the industry. Business processes must be optimized in accordance to specific requirements; operations must be aligned to certify stability within the industry (Harmon, 2007). The demand for energy continually increases; IoT technology once implemented effectively provides visibility into operations worldwide (Deloitte, 2015). IoT applications in the oil industry influences global GDP, adoption of IoT technology can increase global GDP by 0.8% (Oxford Economics). It was found that Oil Company’s digital maturity was at low levels (4.68) (Deloitte, 2015) therefore by developing an integrated strategy in regards to IoT, the industry could acquire significant worth. Data capture and analysis will save money by eliminating over half the company’s well outages and increasing output of crude oil by roughly 10%.

The industries operations are located in remote environments; IoT applications can virtually direct experts to any location in real time. Furthermore the industry faces the loss of experienced workers, new workers replacing retiring workers must adopt technology efficiently to keep pace with consistent growth (Fisher, 2014). Discovery of oil in sufficient quantities is a difficult task, finding and producing hydrocarbons is complex and perceives economic risks. The areas in which they are found are ‘structurally trapped’ and they migrate through the rock leading to lower pressure and low level deposits. The procedure generates vast amounts of data which means the industry must re-design processes to interpret and integrate specific data to stimulate more efficient and accurate decisions. Exploration and production in terms of optimization can gain operational insights from analysis of data. Infrastructure must be connected to wider business software; in-memory computing is critical for combining IoT data with transactional business data in one common database viewed in real time.

IoT-enabled sensors collect data to remotely sense, automate and monitor specific tasks. Connected devices have the ability to process and download new features, manufacturers can modify designs to improve performance and eliminate faulty features (Simplivity, 2015). The oil industry requires extensive real-time action where equipment must be controlled consistently; IoT systems assist managers in terms of monitoring metrics and making decisions. Thus, oil companies are connecting oilrig devices to the Internet; sensors measure pressure and temperature close to the surface and at the deepest points (Talevski, 2009). The aim is to comprehend the quality and flow rate of oil extracted from the surface. The data collected is then analysed from each well instantly as it is sent to the cloud for storage and additional analysis.

Business Processes before implementation

Data was gathered and stored in several different databases, file cabinets and on engineer’s desks. Data captured on every well must be interpreted where there is a large amount of complex information regarding rock quality and existing fluids beneath the surface. Manual collection of data from oil rigs leads to limited accuracy and reliability. The complexity in terms of data lies in the fact that there are excessive amounts that needs to be clarified through integration. The key characteristics in regards to oil monitoring include flow rate, pressure and pipe characteristics.

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Data is primarily requested to be collected from a specific Oil plant to determine if operations are running consistently. Engineers who have access to these specific areas are sent to the refinery to log measurements. The data is manually measured and collected from interpreting the sensors that are connected using a wired network. The wired pressure and temperature sensors allow predictions to be made in regards to the wells functionality. These wired sensors are costly in terms of deployment and operation; engineers must constantly monitor them in remote locations.

If data is successfully collected it is taken back to the main operating station. Once it has reached the station, experts in specific fields inspect the data and process reports on what has been found. The hydrocarbon reservoir data is critical to make decisions in regards to whether it is economical to search for further oil supplies in the specific location. The data is stored by experts at the main station in files and organised systematically.

If errors are found while interpreting data, the engineer in the field must contact the main operating centre discussing the problems involved. Difficulties arise due to complex issues involved with data analysis; experts would rather be in the field than communicating to engineers facing these problems. Advice is given to the engineer in order to resolve these issues in a step-by-step procedure. The specific engineer may not resolve issues therefore a team of specialists must travel to the oil plant to view what has gone wrong and alter processes in order to rectify faults. Once the team has resolved these issues the data is taken to the main operating station and analysed further. The data is mostly paper based thus crosschecks have to be made in regards to specific data which takes considerable time due to the amount of data received from the Oil refinery. Humans are prone to making mistakes and systematic errors can arise if data is not carefully analysed. In an industry such as Oil drilling, errors can impose huge costs that are detrimental at both an industrial and global level. The manual tasks involved lead to a lack of real time data being processed which is critical because faults can arise at any moment, the process of sending an engineer into the field takes time and specific problems cannot be noticed immediately.

Technology description

Sensors analyse data thus optimizing present operations whilst identifying new areas for performance improvements. At each well site, a high speed DDS DAtaBus connects the sensors (flow monitoring and temperature) and actuators (flow controllers and specific drives). The process controller is also connected to these sensors, automating the drilling process and completion. The wireless systems adopt the ISM bands at 2.4 GHz, extensions to PROFIBUS protocol to allow analytical control are analysed using simulations in real-time (Sousa, 2010).  The hardware contains a RAM for dynamic data, flash memories holding codes in the program and long term data, a wireless trans receiver, an ADC, several sensors and a source of power and a micro-controller to compute readings (Stankovic, 2008). Small nodes are within the sensor with a microprocessor (MSP430) and a on-board IEEE 802.15.4 (Ortiz, 2014). The wireless nodes communicate with a gateway using a protocol on IEEE 802.15.4 to allow for routing capabilities extending reliability and distance in regards to the network. The sensors support multiple data consumers allowing several people to access data from the monitoring system at any time or place. The device allows the user to define protocols to third party devices thus ensuring data and network reliability (NI, 2011).

High-speed connectivity allows integration between a remote control centre and the well domain. The wireless fibre network automatically samples the information from the well. The readings are downloaded and stored at the control station for further analysis. The data allows for maintenance that can be predicted, process planners therefore have the proficiency to methodically analyse operations within the well. The data is viewed at the control station and feedback can be sent to the well systems including any corrections made to errors (prior incorrect readings). The remote processes are either restarted or debugged by those in the control centre, this is critical if the local automation is not able to carry out these procedures.

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The data generated from a field of wells contains large amounts of valuable information; this data can be collected across the entire site. The wells across an entire field are integrated by merging local DataBus instances. The system aggregates thousands of sensors that deliver data to the control station leading to monitoring of the systems health, storage of data and quick analysis. DDS DataBus technology supports processes in real time and co-ordinates data collection simultaneously.