Solutions - DAR Solutions

I. Water Management, IOR, and EOR

Optimize waterflooding, manage water shut-offs, and predict well flow with machine learning

Solution	What It Does	Key Features
Waterflood Optimization	Uses Capacitance Resistance Model (CRM) combined with symbolic regression and NPV optimization to recommend optimal injection rates across the field, balancing VRR constraints while maximizing oil recovery	CRM-based production forecasting Automated VRR management NPV maximization with constraints
Water Control & Flow Assurance	Comprehensive solution integrating water problem classification (coning vs channeling), water cut prediction, and well cease-to-flow detection using ML models optimized for early intervention and workover candidate ranking	Water problem type classification WCTF detection (75% recall, 4% false alarms) Integrated workover prioritization

II. Artificial Lift and Production Flow

Predict ESP failures, optimize frequencies, and implement virtual flow meters

Solution	What It Does	Key Features
ESP Failure Prediction	Combines binary classification, RUL regression, and survival analysis models trained on 12,000+ labeled cards to predict ESP failures with 3-week lead time, achieving 90% precision and 4/7 real-world validation success	3-week average lead time Combined failure score from 3 models Scaled to hundreds of wells
ESP Frequency Optimization	Physics-Informed Reinforcement Learning (PIRL) with PPO algorithm optimizes ESP frequency through calibrated mathematical proxy model, achieving 10-15% production increase with consistent recommendations	PIRL with PPO algorithm 10-15% flow rate improvement GEKKO-based calibration
Virtual Flow Metering	Data-driven multivariate and univariate regression models estimate multiphase flow rates (oil, gas, water) from wellhead sensors without physical flowmeters, achieving ~90% R² accuracy with automated data integrity checks	~90% R² accuracy Automated data integrity module Eliminates hardware costs
SRP Health Detection	CNN-based image recognition model trained on 12,000+ labeled downhole dynamometer cards automatically diagnoses pump problems (gas lock, fluid pound, valve leaks) with high accuracy and confidence scores	CNN image recognition 12,000+ training cards Continuous learning capability
Plunger Lift Optimization	ML-based solution for plunger lift systems detecting wear through gas rate prediction models, optimizing setpoints via response surface analysis, and identifying frozen sensors with problem detection scores	Wear detection via prediction error Response surface optimization Velocity control & problem detection

III. Drilling and Development Strategy

Optimize infill drilling locations and completion designs with data-driven insights

Solution	What It Does	Key Features
Sweet Spot Identification	Supervised learning models (LightGBM, tree-based, forest-based) trained on production/injection data, static model features, and ESP/PCP monitoring to systematically identify and rank infill drilling opportunities in heterogeneous carbonate reservoirs	Reliable 1-2 year cumulative predictions Integrates fracture networks & faults Automated candidate evaluation
Gas Price Forecasting	Time series forecasting models (LSTM, Prophet) predict natural gas prices 30-90 days ahead using supply-demand factors (weather, storage inventories) to optimize production timing and NPV analysis for seasonal gas assets	30-90 day forecast window Supply-demand driven predictions Integrated weather & storage data

Comprehensive E&P Solutions

I. Water Management, IOR, and EOR

II. Artificial Lift and Production Flow

III. Drilling and Development Strategy

Ready to Implement These Solutions?