How We Work

A structured path
from data to production
uplift.

Augmentify deploys AI-driven well optimization through a proven five-phase framework — purpose-built for upstream oil and gas operators who need rigor, not guesswork.

Upstream O&G Permian Basin · Eagle Ford Initiate → Assess → Select → Define → Execute

Phase stage-gate lifecycle — from initial scoping to live AI deployment

Lift technologies supported: ESP, rod pump, and gas lift

100%

Data-driven gates — no phase advances without defined quality thresholds

The Augmentify Framework

Built for the reality of oilfield data.

Most AI deployments in oil and gas fail not because of the model — they fail because the data wasn’t ready. Augmentify addresses this directly: our five-phase framework front-loads the hard work of data audit, quality assessment, and gap remediation before a single model is trained. Each phase has defined entry and exit criteria, specific data requirements, and structured deliverables. The result is an AI deployment that works in the field, not just in a demo.

Initiate PH.01

Establishes whether AI well optimization is technically viable and commercially justified for the asset. Data requirements are high-level — enough to characterize the production problem, confirm data availability, and frame the optimization objective. This phase answers the critical go/no-go question before any significant resource commitment is made.

Data Availability Assessment Optimization Opportunity Statement Well Inventory & Lift Register Project Charter

Assess PH.02

The full data landscape is audited in detail. Every source, quality level, and gap is documented. This phase determines whether sufficient historical data exists to train a reliable model, identifies what new instrumentation or data collection is required, and quantifies the data preparation effort. No shortcuts — if the data doesn’t meet threshold, we identify exactly what must be fixed.

Data Audit Report Data Quality Assessment Gap Analysis & Remediation Plan Instrumentation Requirements Model Feasibility Assessment Risk Register

Select PH.03

Audit findings drive the right AI/ML architecture choice, data pipeline design, and technology stack selection. Data requirements shift from collection to integration and structuring — ensuring every source flows reliably into the model pipeline. The architecture is chosen to match the asset’s data reality, not a pre-sold platform.

AI Model Architecture Decision Data Pipeline Design Feature Engineering Specification Technology Stack Selection Vendor / Platform Assessment

Define PH.04

The most data-intensive phase — all data is cleaned, structured, and used to build and validate the AI model. Every input feature is engineered, every training dataset prepared, and model performance validated against held-out field data. Defined acceptance gates must be met before deployment decisions are made.

Cleaned Training Dataset Trained & Validated AI Model Model Performance Report Backtesting Results Deployment Specification

Execute PH.05

The model deploys into the live production environment. Data requirements shift from historical to real-time — the model consumes live sensor feeds for inference, generates optimization recommendations, and feeds actioned outcomes back into a continuous learning loop. Performance is tracked against a defined baseline from day one.

Deployed AI Optimization Model Live Data Pipeline Operator Dashboard Performance Monitoring Report Data Governance Protocol

Applications

Framework applied to every lift type.

The same five-phase discipline governs each Augmentify deployment — with lift-specific data requirements, physics inputs, and model architectures tailored to the equipment in the ground.

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ESP Optimization

VFD setpoint optimization, predictive failure detection, and run life extension for electric submersible pump installations.

View data requirements →

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Rod Pump Optimization

Pump fillage optimization, dynamometer-based failure prediction, and stroke/speed tuning for beam pump and PCP installations.

View data requirements →

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Gas Lift Optimization

Injection rate allocation across constrained headers, valve performance monitoring, and GLPC-based setpoint optimization.

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Design Principles

Why the framework is built this way.

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Data readiness before model development

AI models reflect the data they’re trained on. Phases 1 and 2 are non-negotiable — every engagement starts with an honest audit of what data exists, at what quality, and with what gaps.

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Defined gates, no scope creep

Each phase has explicit entry and exit criteria. No phase advances without meeting the prior phase’s data quality thresholds. This protects both the operator and the model.

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Physics-informed where data is sparse

Where historical data is insufficient for pure ML, Augmentify uses hybrid physics-informed architectures — nodal analysis, multiphase flow correlations, and Thornhill-Craver valve curves — to fill the gap safely.

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Continuous learning, not a one-time deployment

Phase 5 is not the end. Operator override logs, production response data, and field change events feed back into the model continuously. Performance is measured against a defined pre-deployment baseline.

A structured pathfrom data to productionuplift.

Built for the reality of oilfield data.

Framework applied to every lift type.

Why the framework is built this way.

A structured path
from data to production
uplift.