Introduction

Modern oil and gas operations generate enormous amounts of data, but inefficiencies in analyzing and acting on this data can lead to significant production losses. Tasq is an AI-powered operational platform designed to bridge this gap by automating production workflows and improving Decision-making. Deployed at a major operator, Tasq integrated into control rooms, assisted production engineers, and coordinated field operations to create a data-driven ecosystem. The result was a transformation from a manual, reactive workflow to a proactive, AI-driven approach, improving efficiency, production output, and cost savings. Tasq was implemented across all wells in the basin with multiple artificial lift types and well types. Tasq connected 5 different data sources across Scada, Artificial Lift, Production Accounting, Downtime, & Wellview all into 1 system.

Key Results:

• Immediate $1M increase in production revenue within the first month.

• +10× ROI in the first month, scaling to a sustained 100× ROI, with the organization internally reporting a 2,000 bbl/day uplift ($36M/year).

• 2× improvement in downtime reporting, uncovering $75M/month in previously untracked opportunities.

• 30% reduction in downtime, aligning with the primary objective of automating downtime reporting and enhancing operational efficiency.

• Streamlined workflows leading to faster issue resolution and a 3× increase in operational efficiency.

This study provides a deep dive into Tasq’s AI-driven automation architecture, implementation, challenges, and impact, offering insights into how AI is transforming oil and gas operations.

Problem Statement

Key Challenges in Oil & Gas Production Management:

Wasted Time Daily - Poor data quality, increased time dealing with bad data, data entry requirement leads to everyone doing trivial repetitive work. Engineers and field operators had to manually review SCADA data, downtime logs, and work orders, leading to delayed responses and Inefficiencies.

Unreported Downtime – Manual downtime reporting missed small yet cumulative losses, leading to inaccurate production tracking and lost revenue.

Fragmented Software Systems – Data was scattered across multiple siloed tools, requiring excessive manual consolidation.

Reactive Decision-Making – Engineers and operators spent most of their time firefighting issues rather than proactively optimizing production.

High Operational Costs – Inefficiencies in workflow and reporting translated to millions in lost production and unnecessary operational expenditure.

Solution Approach

By integrating AI across operational teams, Tasq eliminated redundant tasks and enhanced efficiency at every stage of production management. Tasq models were deployed to better enhance production, catch unknown issues, prioritize work, automate processes, all to reduce the need of human intervention.

Tasq AI-Models:

Tasq deployed five AI models to automate the flow of work within the control room, engineers, and field operations. These models included:

Well Target Model

ML model that dynamically updates each wells targets without human intervention. For this model,historical trends and real-time data to set optimal production targets for each well were used. With “historical trends, real time data, physics models & user feedback were used across a dynamic feature set for this model.

Results

2x more production opportunity uncovered. Control room operators and engineers were able to view prioritized well issues capturing all leading indicator underperformance that was occurring.

Bulk and Test Prediction Model

ML model trained to project gas and oil rates from the wells pressure data, when the well is not in test.

Results

30% more deferment uncovered than current reporting. Control room operators were able to pin point well degradation or events that happened outside their spot test 24 window, uncovering more production.

Setpoint Optimization Model

Setpoint changes are recommended to optimize a well, providing value in production, while reducing human diagnosis time.

Results

For wells that accept recommendations Optimizers see ~11% production increase to the majority of the

recommendations, reducing optimization time.

Real-Time Model

Tasq catches underperformance live as its happening. Each of these are actionable and yield a live prioritization list, taking tasks out of the system that are also coming back up to target production.                            

Build your own Model

Build models 200x faster with Tasq. Below are a few examples of models that were created by the organization to reduce troubleshooting time.

• Separator Burner Issue Model

Detected and diagnosed burner inefficiencies affecting production efficiency.

• Gas Lift Paraffin in Tubing Model

Monitored and predicted paraffin buildup in gas lift wells, enabling proactive remediation.

• Dump Failure Model

Identified dump valve failures and suggested corrective actions.

• Tank Leak Model

Find leaks before they become a safety critical event

• Arrival Sensor Model

Improved detection for arrival sensor failures, improving plunger run reliability

The next step in operational efficiencies is changing from logic based SQL to learning systems. This is the single most important step in creating the next level of operational efficiencies. It is key that an organization know each production issue at a very detailed level, to route the right work to the right people. There is a high amount of bottleneck that occurs in (a) diagnosing any issue (b) troubleshooting the issues (c) fixing the issue & currently that process is a swiss cheese model limiting value across the

organization. With Build your own model, any user can create their own models to diagnose,troubleshoot, any issue type. Tasq will live classify the issue as its occurring, moving the organization towards higher value work.

Downtime Reduction Analysis

Tasq’s downtime automation provides an accurate, real-time picture of downtime events, eliminating the inefficiencies of manual reporting. Operators previously relied on manual logging, which led to incomplete or inaccurate reporting. With Tasq, every downtime event is automatically detected,categorized, and analyzed, ensuring accurate reporting across all assets.

Results & Performance Gains

Comparing Tasq’s automated downtime tracking vs. manual client-reported downtime, Tasq uncovered 2× more production than what was previously being recorded strictly for downtime. In addition, Tasq AI models identify 4× more production opportunities above what was being reported for current downtime. Once Tasq was deployed, there was a clear slope change in downtime %. Production was recovered much quicker with Tasq’s AI prioritization than before. Tasq reduced downtime by 30% vs previous baseline (white).

Tasq changed the slope of % downtime right from onboarding and consistently after.

Model Driven Approach Reduces Downtime

Models driving work assignment decreases downtime volume across operations

Results

30 % reduction in total downtime within six months.

Manual Reporting is a Failed Strategy

Reliance on employees to fill out downtime fails in many ways including incorrect underreported volume reporting, ineffective use of employees time, & missed opportunities

Results

Tasq surfaced 2x more downtime that what was being reported and 4x more total opportunity.

Simplifies Workflows

Tasq decreases human touch by 3x by connecting different data sources & automating workflows. This approach simplifies operations for end users & increases actionability.

Results

Lesser user steps to capture downtime accurately

Conclusion & Summary

Tasq has transformed production management by integrating AI-driven automation to improve operational efficiency and drive significant cost savings.

Key Takeaways:

Automated Downtime Detection & Reduction:

Tasq uncovered 2× more downtime events and led to a 30% reduction in downtime over six months.

Operational Efficiency & Production Gains:

Operators no longer waste time manually entering downtime data—Tasq automates the process. Engineers & control room focuses on high-impact actions rather than sifting through inconsistent data.

Significant ROI & Production Uplift:

$1M in new production revenue was generated in the rst month. Sustained benets resulted in a +10× ROI in month one, scaling to 100× ROI. 2,000 bbl/day production increase, equating to $36M/year in added value.

AI-Driven Decision-Making & Alignment:

Teams work from a single source of truth, eliminating guesswork. The platform provides real-time recommendations, ensuring optimal production outcomes.

About Tasq

Tasq is built on the belief that the future is model-driven, and access to AI shouldn’t be limited to a handful of data scientists. Organizational bottlenecks slow productivity and keep critical insights out of the hands of the people who need them most. Tasq platform empowers entire teams to build, deploy, and iterate on AI models and workflows—without waiting in line. Data can be uploaded and live models deployed in minutes, giving teams the speed and autonomy to generate real value, fast. 

Tasq was developed by engineers who led AI initiatives in the Oil and Gas industry & are creating the future for operations.