SoftServe and TS Cyanergy partner to reduce engine emissions in solution powered by AWS

Evolving regulation is making accurate reporting more important than ever

The oil and gas industry’s combustion emissions are a contributor to the greenhouse gas (GHG) emissions that are a cause of climate change. Tracking and reporting of these emissions is necessary for oil and gas operators to measure and reduce their environmental impact and satisfy regulatory requirements. In many countries, governments have set targets to reduce GHG emissions, and regulators are increasingly implementing policies to incentivize companies to measure, report, and reduce their emissions. Failure to comply with these regulations can result in financial penalties, reputational damage, and legal liabilities. By accurately tracking and reporting their combustion emissions, oil and gas operators (and their service providers) can better understand their environmental impact, identify areas for improvement, and develop strategies to reduce their GHG emissions.

In the United States, the US Environmental Protection Agency (EPA) mandates that oil and gas operators report their emissions of criteria pollutants, including nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM). There are additional requirements around reporting GHG emissions including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The reporting requirements vary depending on the size and type of the facility, in addition to other site-specific regulations. Some states have their own reporting requirements for oil and gas operators in addition to federal regulations. For example, the Texas Commission on Environmental Quality (TCEQ) requires operators to report their emissions of air pollutants, including GHGs, under the Texas Clean Air Act. Other states have similar requirements under their own air-quality regulations.

Tracking emissions efficiently and effectively is not easy. A specific challenge area is measurement and management of diesel engine emissions. Diesel engines are complex machines, and there is opportunity to capture multiple datasets from a single engine. The most common practice for calculating emissions from diesel engines is approximating emissions based on fuel volume consumed and relating that to data tables provided by an engine manufacturer. This approach leads to both over- and underreporting of emissions. Inaccurate measurement and reporting results in financial, legal, and/or reputational consequences for customers. SoftServe and TS Cyanergy partnered to provide precise engine-emissions measurement, minimize risk associated with inaccurate reporting, and optimize engine performance for their customers in a solution powered by Amazon Web Services (AWS).

Addressing emission measurement challenges

There are technical challenges to facilitating effective and efficient engine management. These challenges include implementing direct emission measurement at the source using Internet of Things (IoT) devices, collecting equipment operational data, and merging these datasets against a common context. After the data is consolidated, the next question becomes, “What do you do with it?”

Streaming this data to the cloud for data processing and calculation by using tools like the Guidance for Carbon Data Lake on AWS can help operators automate emission reporting and better understand their emission profile. Combining tools provided by AWS and TS Cyanergy, along with expertise provided by SoftServe, customers are identifying and capitalizing on opportunities to reduce their carbon footprint.

Solving the problems

SoftServe and TS Cyanergy introduced a solution that offers near-real-time and precise emission measurement, providing insights to optimize both onshore and offshore production activities. The TS Cyanergy solution aggregates onsite data, facilitating effective management and environmental, social, and governance (ESG) reporting, and empowering companies to make informed decisions to reduce their carbon footprint and improve their sustainability performance.

TS Cyanergy installs its sensor-based equipment to measure emission levels from combustion engines that are used by operators to power equipment such as pumps, compressors, generators, and drilling rigs. Caterpillar Inc. (CAT) engines are commonly used for those activities.

• CAT 3512C HD engines are a popular choice for offshore drilling rigs and power-generation applications.
• CAT 3516C HD engines are commonly used for powering pumps, compressors, and other equipment at oil and gas production facilities.
• CAT C27 ACERT engines are popular for powering fracturing equipment, cementing units, and other equipment used in hydraulic fracturing operations.

TS Cyanergy has developed an edge infrastructure that is highly effective at gathering data from emission sensors and engines. This infrastructure is integrated with the AWS Cloud for advanced data processing and storage. The design of the infrastructure is carefully optimized to reduce costs and minimize the need for extensive equipment.

To fully use measured emission data and to provide guidance and recommendations on how to optimize engine performance, TS Cyanergy set out to develop a solution to achieve the following customer (end-user) goals:

• Make decisions regarding optimization with confidence
• Reduce fuel consumption and GHG emissions
• Eliminate paperwork
• Facilitate scalability
• Save employees’ time on data collection
• Improve engine productivity

TS Cyanergy teamed up with SoftServe to deliver a first proof of concept to better understand customer requirements, validate assumptions, and prepare a road map. After a successful proof of concept, TS Cyanergy and SoftServe implemented a production-grade first phase that delivered results.

The team automated emission measurement and proactive optimization for power generation for a drilling rig.

Figure 1

In figure 1, the process begins by running the engines on a specific rig and connecting all the edge devices to these engines. As the engines operate, measurement data is continuously collected and recorded. This measured data is then stored in comma-separated values (CSV) files.

Next, the gathered CSV files are sent to a bucket in Amazon Simple Storage Service (Amazon S3), an object storage service, through file transfer protocol (FTP). These files encompass comprehensive information gathered from all edge sensors connected to the engines. This includes data such as the date and time of each measurement taken, as well as details regarding exhaust gases.

To consolidate the collected data, a data-merge activity is performed within this repository using AWS Lambda—a serverless, event-driven compute service. This merging operation combines the individual CSV files into a unified dataset that will be used by the application for future calculations.

After the merging operation is successfully completed, the application initiates the calculation process.

The application assumes the responsibility for providing accurate calculation processes. After merging and securely storing all relevant CSV files in the designated Amazon S3 bucket, the application can seamlessly retrieve the required data from the bucket. By employing predefined mathematical formulas, the application can then automatically perform the necessary calculations.

This process employs predefined algorithms and formulas to analyze the data and generate the desired results. The calculated outcomes are then stored in an Amazon Relational Database Service (Amazon RDS), a collection of managed services.

Subsequently, users who are logged on to the Tableau application, under the permissions assigned by the TS Cyanergy administrator, can access and view the necessary data. In the proposed system, every user will be categorized into one of six distinct user groups: rig operator, rig superintendent, ESG analyst, operations manager, auditor, and executive. Each user will have a specific role and corresponding access privileges within the system. For secure access, authentication will be performed through a combination of email address and a personal domain password.

Tableau establishes a connection to Amazon RDS and retrieves the relevant data. This helps users to create different dashboards and reports based on the retrieved data, empowering them to visualize and analyze the information effectively.

The solution was engineered to accommodate a variety of user personas, as evidenced by the user dashboards. The solution includes dashboards for analyzing engine load, fuel consumption, and emission statistics. The dashboards were built to cater to personas, including rig operators, rig superintendents, ESG/engineering, Cyanergy business insight (BI) analysts, operations managers, and auditors. Further, CSV reports can be exported for necessary deep dives.

Figure 2

Figure 2 represents operator dashboards that indicate the number of engines in operation. The first image indicates that one engine works, and it is the efficient regime of work as indicated by the green color. The second image displays that we need to switch up to two engines, because the yellow color indicates the inefficient-power-load mode. The third picture indicates that we need to switch up to three engines, because the red color indicates the highly inefficient-power-load mode. The indications show only efficient and inefficient operations without overheating.

The system acts as an indicator that some action should be performed to continuously maintain the green power-load mode. The rig operator is responsible for communicating in time with the person in charge of switching the power-load modes on onsite CAT diesel engines.

Delivering results

SoftServe helped TS Cyanergy reach its main objectives and achieve tangible outcomes.

• Enhanced engine productivity
• Reduced paperwork
• Scalability up to 90,000 active units (estimated number of engines in operation)
• Improved operating efficiency
• Cost savings and cost reductions
• Improved accuracy and quality

The system already incorporates all the necessary sensors required for emission measurement, and there is no need to introduce additional sensors in the future. These predefined sensors cover all the essential parameters needed to accurately measure emissions.

Conclusion

The proposed solution exerts a direct influence on the mitigation of GHG emissions. In simpler terms, implementing the green power-load mode across all drilling rigs would optimize the engine performance, resulting in reduced consumption of diesel fuel. Consequently, this would lead to a corresponding decrease in emissions from diesel combustion. If this technology were implemented universally on drilling rigs, the positive impact on GHG emission reduction would be substantial.

Furthermore, it is important to acknowledge that the process of evaluating and precisely calculating emissions is far from flawless. Nonetheless, by employing this software, we not only achieve emissions reduction but also establish a robust accounting process. In essence, all stakeholders, including personnel within the company and government institutions, can have confidence in the accuracy and authenticity of the emission values.

Countries establish their targets for sustainability in diverse ways, considering their unique circumstances. Broadly speaking, two overarching targets can be identified: (1) reduction of emissions and (2) accurate accounting of emission values.

Check out this case study to learn more about how SoftServe helped TS Cyanergy use AWS to increase diesel engine productivity by 25 percent while simultaneously minimizing engine emissions.

Enhancing engine performance and reducing emissions are just the start for SoftServe, TS Cyanergy, and AWS. To learn more about the results that this group is driving together, contact the AWS Energy team.