- Software Development
- AI & ML
- Data Analytics
- Platform Development
- Real-Time Solution
- Big Data
An international mining company was looking for oil and gas analytics software that could bring together data from SCADA systems, IoT sensors, and drilling logs. What we introduced was a real-time predictive platform. It resulted in 15% higher production efficiency, 20% lower downtime, and a 30% decrease in maintenance costs.
client
NDA Protected
USA
130 employees
Our client is a global leader in the oil and gas business with key interests in exploration, drilling, and production. In addition, it has operations that span the entire continent(s) of Europe and South America and elsewhere in over countries, including offshore production facilities, onshore wells, production fields, and reservoirs.
request background
Energy Data Analytics Software to Boost Efficiency with Informed Decisions
The customer was in need of oil and gas analytics software to solve the problem of operational inefficiencies due to data being scattered across disparate sources. Different monitoring and reporting systems were in place at all the sites. Engineers found it difficult to get a comprehensive view of how well everything was working across all operations worldwide. Without integrated data management, the firm lost out on drilling optimization and preventive maintenance.
Increased market volatility and regulatory pressures required more cost discipline. Equipment failures caused expensive downtime. Panchromatic prediction models resulted in unreliable estimates of production. Leadership wanted something they could act on to make decisions and take action fast.
challenge
A Scalable Platform with Comprehensive Oil and Gas Data Analysis
Disparate data from SCADA, IoT, and drilling logs. Each geographical location used different monitoring systems. SCADA platforms tracked equipment status, IoT sensors measured environmental conditions, and drilling logs contained geological information. These sources operated independently with no integration. Engineers spent days compiling reports manually.
No unified approach to data management. Historical production records sat in legacy databases. Real-time streams are never connected to analytical tools. The company lacked a centralised repository where teams could access complete information. This fragmentation delayed decision-making and obscured performance trends.
Low estimation accuracy. Production planners relied on spreadsheets and historical averages. These methods failed to account for reservoir behaviour changes or equipment degradation. Prediction errors led to missed production targets and inefficient resource allocation.
Equipment downtime risks. Reactive maintenance meant fixing equipment after failures occurred. Unplanned outages disrupted production schedules and caused revenue losses. The company had no way to predict when critical components would fail.
Compliance and regulatory requirements. The oil and gas industry faces strict reporting obligations. Without proper management of data and audit trails, demonstrating compliance became labour-intensive. Security concerns around sensitive operational data added complexity.
goals
- Implement real-time data integration from all sources across global operations
- Deploy machine learning-powered predictive analytics for equipment and performance
- Build estimation models for drilling operations and reservoir data analysis
- Create scalable architecture supporting current needs and future expansion
- Deliver actionable insights through intuitive reporting dashboards
solution
The Ultimate Tool for Real-Time Oil and Gas Data Analysis
.NET, ASP.NET Core, React.js, Redux, D3.js, PostgreSQL, Apache Hadoop, Apache HBase, AWS, EC2, S3, RDS, Redshift, Apache Kafka, Apache Spark, TensorFlow
19 months- 9 specialists
We developed comprehensive analytics software that unifies data streams and applies advanced analytical methods. Apache Kafka and Apache Spark handle real-time data integration from SCADA systems, IoT sensors, drilling logs, and production records. This creates a unified repository supporting sophisticated analysis.
With TensorFlow training, drilling can be optimized and equipment maintained using predictive analytics models. The forecasting engine examines reservoir data to forecast production rates and determine optimal drilling parameters. The recommendations to the engineers operate off current conditions and historical patterns.
The platform architecture emphasises cloud scalability using AWS services. Storage layers accommodate massive data volumes while analytical processing scales dynamically with workload demands. This approach supports operations across multiple continents without performance degradation.
React.js and D3.js power the visualisation layer. Custom dashboards present complex information clearly to engineers, geologists, and managers. Real-time monitoring displays update continuously, while reporting tools generate exports in CSV and PDF formats. Similar analytical capabilities appear in our AI-powered renewable asset monitoring software for the energy sector.
- We developed a scalable architecture to manage large data volumes and facilitate future system expansions, supporting long-term growth.
- Our developers ensured that the chosen architecture was compliant with the energy industry regulatory policies.
- Advanced analytics were applied to anticipate equipment failures, substantially reducing maintenance costs and downtime.
- Built following machine learning best practices, the oil & gas analytics software provided engineers and geologists with actionable insights, improving asset management and fostering informed decision-making.
- We ensured the solution was adaptable to evolving business needs and technological advancements, maintaining its relevance and effectiveness over time.
- Focused on convenient user flow, Acropolium developed an intuitive UI with React.js and D3.js to present data and insights effectively to users.
- Lastly, we conducted extensive testing to ensure reliability, accuracy, and performance, followed by iterative optimizations.
outcome
Optimized Workflows & Cost Savings through Advanced Analytics in Oil and Gas Operations
- 15% increase in production efficiency. Better prediction of sights and drilling optimization reduced the time to reach production targets. Engineers applied data-driven recommendations that improved well performance. Resource allocation became more effective across the portfolio.
- 20% reduction in equipment downtime. Predictive analytics identified potential failures before they occurred. Maintenance teams performed preventive maintenance during scheduled windows. Unplanned outages decreased significantly, improving production consistency.
- 30% cost savings in maintenance. Shifting from reactive to preventive maintenance reduced emergency repair expenses. Longer equipment life cycles lowered replacement costs. Better planning minimised overtime labour charges.
- Improved forecasting accuracy. Reservoir data analysis and machine learning models produced more reliable production estimates. Planning teams were able to make smarter decisions with respect to capital investments and operational tactics. The forecasting error rate was reduced by more than 40% versus other approaches.
- Enhanced transparency for leadership. Managers had a bird’s-eye view of operations with consolidated dashboards from all locations across the globe. They were able to isolate underperforming assets sooner and deploy resources more strategically. Data management capabilities supported faster decision-making at all organisational levels.
client feedback
We are beyond happy with the brand-new oil and gas data analysis software. Thanks to Acropolium's agile approach and attention to detail, we have gained operational insights to achieve new levels of efficiency and reliability. Now, we're operating even smoother and get the most out of data while saving costs. Saying that we're impressed would be an understatement!
