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Advanced Real-Time and Multi-Physics Engineered Bullhead Sand Consolidation Using SacTi Resin with AI-Assisted Candidate Screening for Ultra-Complex Swamp Gas Wells
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Abstract
Sand production remains one of the most critical challenges in swamp gas wells, particularly in reservoirs characterized by unconsolidated formations, high water drive, and complex well trajectories. In the Mahakam Delta, these challenges are further intensified by highly deviated well profiles, limited intervention access, and narrow operational margins, increasing the risk of sand influx, wellbore instability, and premature production decline. Conventional sand control methods, such as mechanical installations and rig-based interventions, are often constrained by high operational costs, logistical complexity, and extended non-productive time (NPT), making them less attractive for mature and marginal gas field developments.
This paper presents an Advanced Real-Time and Multi-Physics Engineered Bullhead Sand Consolidation (SCON) methodology using SacTi resin for sand control in mature swamp gas wells. To improve candidate selection and treatment design, a regression-based screening workflow and AI-assisted Bullhead Suitability Score (BSS) framework were developed using injectivity-test data, effective permeability estimation, pressure stability, and fracture-margin evaluation. The workflow transforms field injectivity observations into a quantitative decision-support tool for candidate ranking and treatment optimization.
Injectivity-test regression analysis identified the relationship between pumping rate and effective permeability and established an optimum operating window of approximately 1.0–1.2 BPM for matrix resin placement. The regression-based AI-assisted screening workflow successfully differentiated candidate quality, with Well AR-1 achieving a BSS of 95 and Well AR-2 achieving a BSS of 70, indicating favorable bullhead treatment suitability for both wells.
Field implementation in wells AR-1 and AR-2 demonstrated successful resin placement and accelerated curing performance. Compressive strength reached approximately 1,095 psi after 5 days and increased to 2,266 psi after 14 days, confirming strong consolidation capability. Post-treatment monitoring showed zero measurable sand production and stable gas production exceeding one year, with average gas rates of approximately 2.7–3.7 MMSCFD while maintaining favorable permeability retention.
From an economic perspective, the rigless bullhead methodology generated cost savings exceeding USD 20,000 per well compared with conventional intervention methods. The integration of regression-based analytics, AI-assisted candidate screening, and engineered bullhead execution demonstrates a practical and scalable framework for optimizing sand control treatments in mature unconsolidated gas reservoirs.
Title: Advanced Real-Time and Multi-Physics Engineered Bullhead Sand Consolidation Using SacTi Resin with AI-Assisted Candidate Screening for Ultra-Complex Swamp Gas Wells
Description:
Abstract
Sand production remains one of the most critical challenges in swamp gas wells, particularly in reservoirs characterized by unconsolidated formations, high water drive, and complex well trajectories.
In the Mahakam Delta, these challenges are further intensified by highly deviated well profiles, limited intervention access, and narrow operational margins, increasing the risk of sand influx, wellbore instability, and premature production decline.
Conventional sand control methods, such as mechanical installations and rig-based interventions, are often constrained by high operational costs, logistical complexity, and extended non-productive time (NPT), making them less attractive for mature and marginal gas field developments.
This paper presents an Advanced Real-Time and Multi-Physics Engineered Bullhead Sand Consolidation (SCON) methodology using SacTi resin for sand control in mature swamp gas wells.
To improve candidate selection and treatment design, a regression-based screening workflow and AI-assisted Bullhead Suitability Score (BSS) framework were developed using injectivity-test data, effective permeability estimation, pressure stability, and fracture-margin evaluation.
The workflow transforms field injectivity observations into a quantitative decision-support tool for candidate ranking and treatment optimization.
Injectivity-test regression analysis identified the relationship between pumping rate and effective permeability and established an optimum operating window of approximately 1.
0–1.
2 BPM for matrix resin placement.
The regression-based AI-assisted screening workflow successfully differentiated candidate quality, with Well AR-1 achieving a BSS of 95 and Well AR-2 achieving a BSS of 70, indicating favorable bullhead treatment suitability for both wells.
Field implementation in wells AR-1 and AR-2 demonstrated successful resin placement and accelerated curing performance.
Compressive strength reached approximately 1,095 psi after 5 days and increased to 2,266 psi after 14 days, confirming strong consolidation capability.
Post-treatment monitoring showed zero measurable sand production and stable gas production exceeding one year, with average gas rates of approximately 2.
7–3.
7 MMSCFD while maintaining favorable permeability retention.
From an economic perspective, the rigless bullhead methodology generated cost savings exceeding USD 20,000 per well compared with conventional intervention methods.
The integration of regression-based analytics, AI-assisted candidate screening, and engineered bullhead execution demonstrates a practical and scalable framework for optimizing sand control treatments in mature unconsolidated gas reservoirs.
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