Stimulation and Chemical Treatment
Wednesday, 21 October
Room 361 BECF
Technical Session
This session delivers a fast-paced look at breakthrough stimulation and chemical-treatment technologies shaping today’s subsurface operations—from nano-silica water-shutoff treatments and nanoparticle-driven plasma stimulation for lower-emission frac fleets to innovative calcium-carbonate prenucleation methods for caprock mitigation. Speakers will share advances in matrix-acidizing accuracy, insights from extensive retarded-acid experimentation, and lessons learned from hydraulic-fracturing campaigns. The program also highlights improvements in produced-water interface chemistry, digital slickline applications for corrosion monitoring, and rapid fracture-geometry prediction using convolutional-neural-network proxy models, offering attendees a sharp snapshot of the innovations redefining reservoir intervention.
Session Chairpersons
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1400-1425 233849Integrated Hpt-aps Logging And Nano-silica Treatment For Enhanced Water Shut-off In Complex Reservoirs
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1425-1450 234114Toward Net-zero Frac Fleets Using Nanoparticle Based Pulsed-power Plasma Stimulation
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1450-1515 234173In-situ Nucleation And Growth Of Calcium Carbonate Prenucleation Clusters For Caprock Mitigation In Deep Saline Aquifers
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1545-1610 234140Improving High-rate Matrix Acidizing Performance Through Fluid Friction Calibration And Downhole Pressure Prediction Accuracy
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1610-1635 233818Laboratory Evaluation Of Single-phase Retarded Acid System Performance In Limestone And Dolomite Rock - Part 4: Learnings From 280+ Experiments
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1635-1700 233822Advances In Hydraulic Fracturing In Block 61, Succesfull And Unsuccessful Study Cases From Ecuador
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Alternate 233921Improvement Of Oil-Water Interfaces In Produced-Water Systems Using Multifunctional PHC-THPS Chemistry For Iron Scavenging And Microbial Control
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Alternate 233919Field Deployment Of An Integrated Digital Slickline For Time Lapse Corrosion Logging In A Mega Field
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Alternate 233997Rapid Prediction Of Hydraulic Fracture Geometry In Horizontal Wells Using A Convolutional Neural Network Proxy Model


