Company

Oilfield Digitalization Award

We are pleased to announce that our efforts to introduce digital technologies into the oil production have been highly valued and we have received the Oilfield Digitalization Award at the International Digital Forum Almaty!

This is indeed a valuable prize and worthy assessment of our company's contribution to the development of digital technologies and a clear indicator that our company is able to adapt to new challenges and opportunities.

We are proud to say that our company is a kind of pioneer in the field of digitalization of the oil field. However, we are not going to stop there. We have big plans for the future.

Yerzhan Tazabekov, General Director to KAZPETROL GROUP LLP:

"I would like to note that we are receiving the award for the second year in a row at this forum. And this is, first of all, the merit of our employees! We pay great attention to the development of the smart deposit, and as a result, our company can already call itself an active participant in the industrial revolution “Industry 4.0!”

The Digital Almaty Forum is a traditional dialogue platform for discussing the digital agenda, new digital transformation strategies and trends in the field of digital technologies. We were glad to take part in this event and share our experience with other leaders and experts.

About the project:

Automated Information Collection and Processing System (AICPSI) DIGITAL SMART OILFIELD: A Revolution in the Oil Industry

The oil industry is constantly improving, introducing the latest technologies to increase efficiency and improve management. One of the striking examples of innovations in this area is the Automated Information Collection and Processing System (AICPSI) DIGITAL SMART OILFIELD, the project implemented by “KAZPETROL GROUP” LLP.

Introduction to the project

The main goal of the project is to create a decision support system based on "big data", a platform and machine learning algorithms. This change in the operating environment is aimed at moving from solving routine tasks using manual labor to scenario modeling using digital solutions.

Achieved project goals

The project has achieved a number of important goals, positively affecting the operation of the entire field:

• An increase in the well exploitation coefficient by 5%: Thanks to automated control and monitoring systems, it was possible to optimize the operating mode of each well, which led to an increase in the exploitation coefficient.

• Reduction of oil production losses: Minimizing shut down and timely response to possible problems reduced oil production losses, which had a positive impact on the overall productivity of the field.

• Optimization of production costs by 10%: The use of an analytics and machine learning system helped to identify effective ways to optimize production costs, which immediately affected the project budget.

• Increased inter-repair work period of wells: Monitoring and predictive analytics systems made it possible to effectively plan routine maintenance, which increased the intervals of inter-repair work period of wells.

• Improving the efficiency of management decisions by 100%: The introduction of a decision support system based on big data has improved the quality and accuracy of management decisions, which has led to an increase in the overall efficiency of the field.

The main steps of the project

1. The instrumentation of the wellhead has been completed:
• Critical area measurement systems have been installed.
2. Implementation of analytical systems:
• Field development;
• Analysis of downtime losses;
• Accounting for the reverse distribution of oil.
3. Centralized data collection:
• Organization of centralized data collection from distributed production facilities;
• Implementation of a decision support system based on "big data"
4. Development and purchase of software for the implementation of the project:
• Integration of I-mass with the telemetry system and t-Nav analytical tools;
• Integration with the OFM system (control over the management and development of deposits)
5. Automation of monitoring:
• Drilling monitoring system;
• Automated liquid balance accounting system;
• Automatic multi-well optimization system.
6. Intelligent decision support system:
• Telemechanization of wells;
• Telemechanization of the AGZU;
• Dispatching of technological facilities;
• Online control and monitoring.
7. Digitization of production data:
• Central control room;
• Analytics and automatic reporting.
8. Intelligent mining management:
• Intelligent production level management system;
• Predictive analytics using neural networks and artificial intelligence.

Project results

Impressive results have been achieved after the implementation of the project:

• Reduction of oil losses due to timely response;

• Increase in the time to failure of DPE;

• Potential additional production due to the diagnosis of DPE;

• Remote monitoring of measuring installations;

• Reduction of time for data collection for operational work;

• Improving the quality of reverse distribution;

• Process planning based on accurate and up-to-date data;

• Optimization of production reporting;

• Automated liquid balance accounting system;

• Centralized data collection from distributed production facilities, manual data collection.

Implementation of well telemetry

The process of implementing well telemetry included:

1. Preparatory work, including the purchase of equipment and materials, as well as the manufacture of masts;

2. Development of trenches and a pit for the communication mast;

3. Installation of the communication mast with the laying of HDPE pipes, pipe and galvanized trays;

4. Laying of cable products from the communication mast to the control system;

5. Installation and configuration of the control system;

6. Installation and connection of the rocking machine alarm and dynamometer system;

7. Installation and configuration of radio equipment.

Results of PDM MES implementation

The introduction of PDMS IMASS allowed:

• Build a field infrastructure through a diagram;

• Collect data on equipment stops and infrastructure facilities;

• Collect information related to laboratory research;

• Calculate the difference in flows regularly for each object;

• Collect data on the movement of liquid in tankers;

• Collect data on liquid measurements in the AGZU;

• Calculate well flow rates based on operational data;

• Calculate the balance for each fluid.

Impact on the operation of the field

The project has significantly affected the production and optimization of the entire field:

• Improved reverse of oil flow: Analytics and machine learning systems helped to more accurately determine the characteristics of the field, which affected the quality of reverse oil flow and increased its efficiency;

• Process planning based on accurate data: Big data analysis made it possible to plan the process more accurately, which reduced risks and increased the stability of production;

• Automated Liquid balance accounting system: The introduction of an automated liquid balance accounting system has provided accurate and reliable monitoring of expenses and receipts throughout the field;

• Improving the quality of reverse distribution: The use of predictive analytics and artificial intelligence improved the quality of reverse distribution, which affected the stability and reliability of the entire mining system;

• Optimization of production reporting: Automated reporting and analytics have helped to create more reliable and informative reports, simplifying the strategic decision-making process.

Conclusion

The DIGITAL SMART OILFIELD project is a prime example of innovative technologies introduced into the oil industry. Its successful implementation has led to a significant increase in efficiency, reduction of losses and optimization of production processes. This experience can serve as a model for other companies seeking to introduce advanced technologies to improve their oil production activities.

The DIGITAL SMART OILFIELD project has not only transformed the ways of oil production, but also raised the industry to a new level of efficiency and innovation. This success is a vivid example of how modern technologies can make mining more efficient, sustainable and environmentally friendly.