Innovative Remedial Sand Control: Maximizing Barrel Output with Retrofit Techniques

Sand production continues to be a significant challenge in the oil and gas industry, especially as more output is derived from aging and mature fields. This issue often arises due to the failure of primary sand control measures or the onset of sand failure, which can be attributed to reservoir depletion and increased water production. Reservoir depletion reduces the formation pressure, leading to the destabilization of sand grains within the reservoir rock. Concurrently, increased water production can exacerbate the problem by further weakening the formation and facilitating the movement of sand particles into the wellbore. To address sand production, operators typically manage it at the surface, implement downhole sand control measures, or use a combination of both strategies. Remedial solutions aim to minimize sand production while maintaining the productivity of existing wells. Furthermore, these solutions must be both cost-effective and reliable, ensuring minimal downtime and avoiding frequent retreatment or reinstallation. The selection and design of downhole sand control measures require meticulous consideration of reservoir characteristics, production conditions, and economic factors. Various downhole remedial solutions are available, including mechanical and chemical methods that can be deployed through existing well completions. Examples include through-tubing sand control, sand consolidation, and agglomeration techniques.

From this session, we can gain valuable insights into downhole remediation solutions, comprehensive overview of various techniques and strategies used to address sand production issues in the oil and gas industry covering Selection and Design Processes, Execution: Results Analysis, Sharing New Technologies and Case Histories.

The likelihood of sand production increases, as oil and gas fields mature, posing substantial challenges to sustaining operational efficiency, ensuring production uptime, and safeguarding the integrity of facilities. Recognizing that effective sand monitoring is fundamental to a holistic and integrated sand management strategy, the “Sand Monitoring and Digitalisation” session will showcase the latest advancements in monitoring technologies alongside the transformative role of digitalisation in addressing these complex challenges.

This session intends to highlight the applications of sand monitoring technologies and best practices, such as surface and downhole sand detectors, erosion probes, online sand samplers and flowline thickness monitors, among others in providing real-time information for mitigating sand related challenges. Participants will gain insights into how these technologies can be seamlessly integrated into the day-to-day operations of an asset, providing precise and timely data essential for proactive decision-making. By leveraging these tools, operators can effectively minimize the risks of Loss of Primary Containment (LOPC), equipment damage, and production downtime.

Building on conventional sand monitoring approaches, this session will also showcase the impact of digitalisation—specifically AI and machine learning—to further advance the practice of sand management. These innovations employ predictive analytics to forecast sand production, identify risks, and deploy automated solutions, minimizing manual intervention while enhancing precision and reliability. By improving efficiency, cutting costs, and reducing environmental footprints, AI-driven digitalisation will be critical towards improving sustainability, revolutionizing how operators approach sand monitoring and management in today’s dynamic oil and gas landscape.

The likelihood of sand production increases, as oil and gas fields mature, posing substantial challenges to sustaining operational efficiency, ensuring production uptime, and safeguarding the integrity of facilities. Recognizing that effective sand monitoring is fundamental to a holistic and integrated sand management strategy, the “Sand Monitoring and Digitalisation” session will showcase the latest advancements in monitoring technologies alongside the transformative role of digitalisation in addressing these complex challenges.

This session intends to highlight the applications of sand monitoring technologies and best practices, such as surface and downhole sand detectors, erosion probes, online sand samplers and flowline thickness monitors, among others in providing real-time information for mitigating sand related challenges. Participants will gain insights into how these technologies can be seamlessly integrated into the day-to-day operations of an asset, providing precise and timely data essential for proactive decision-making. By leveraging these tools, operators can effectively minimize the risks of Loss of Primary Containment (LOPC), equipment damage, and production downtime.

Building on conventional sand monitoring approaches, this session will also showcase the impact of digitalisation—specifically AI and machine learning—to further advance the practice of sand management. These innovations employ predictive analytics to forecast sand production, identify risks, and deploy automated solutions, minimizing manual intervention while enhancing precision and reliability. By improving efficiency, cutting costs, and reducing environmental footprints, AI-driven digitalisation will be critical towards improving sustainability, revolutionizing how operators approach sand monitoring and management in today’s dynamic oil and gas landscape.

With increasing complexity in well conditions and a growing focus on production efficiency, surface sand handling has emerged as a key component of sustainable field development. This session will explore the latest advancements in surface sand management, including the integration of automated and autonomous systems, real-time data acquisition, and AI-driven decision-making to enhance operational performance and reduce human intervention.

Discussions will cover a range of technologies such as compact and modular separation units, remote-controlled systems and predictive maintenance enabled by machine learning. Real-world case studies will demonstrate how these innovations are minimizing downtime, improving safety, and enabling operators to produce from sand-prone wells with greater confidence.