Relevant experience

The experience behind Melo Energy’s capabilities.

Selected examples show the delivery setting, contribution, and evidence that transfer into future consulting assignments.

Experience context, not endorsement. The portfolio combines work delivered through Melo Energy with experience brought into the company from its founder’s prior roles. Organization names and logos identify professional context. They do not represent current Melo Energy clients, partnerships, or endorsements.

How to read the experience

The record spans three types of relationship.

The distinction separates Melo Energy delivery from founder experience, project environments, and technology familiarity without implying a current commercial relationship.

Company and founder record

Melo Energy delivery and prior roles

Melo Energy has led three AI initiatives for national oil companies in reservoir management and production optimization. The broader record also includes experience from Halliburton, C3 AI, Xecta, and AspenTech/Emerson.

Project environments

Organizations connected to prior work

Selected projects include environments associated with ADNOC and ZADCO, Petrobras, PTTEP, TotalEnergies, and other accurately labeled career contexts.

Technology ecosystem

Tools and platforms used in delivery

Experience includes major reservoir, well, network, cloud, data, and AI technologies. Software names indicate technical familiarity, not vendor certification or endorsement.

Selected delivery evidence

Context, contribution, and value.

01

Virtual rate estimation · Abu Dhabi

AI-driven rate estimation across 800+ wells

ZADCO, now part of ADNOCExperience brought into Melo Energy from a prior Halliburton role
Context

A smart-field initiative needed consistent production-rate estimates across a large well population.

Delivery contribution

Melo Energy's founder built core C++ systems and managed the ETL workflows supporting the engineering solution.

Demonstrated value

The work combined production-engineering logic, data workflows, and AI at field scale.

02

Integrated capacity model · Abu Dhabi

Integrated production modeling for energy assurance

ADNOCExperience brought into Melo Energy from a prior Halliburton role
Context

An operator required a connected view of asset models and operating constraints for production forecasting.

Delivery contribution

Melo Energy's founder designed an AI-integrated asset model and the technical workflow around it.

Demonstrated value

The model supported production forecasting and national energy-assurance decisions.

03

Predictive maintenance · Brazil

A delayed AI pilot recovered within two months

Industrial AI programExperience brought into Melo Energy from a prior C3 AI role
Context

A cloud-based predictive-maintenance program required delivery recovery and renewed stakeholder alignment.

Delivery contribution

Melo Energy's founder led the cross-functional recovery effort and brought implementation back on track.

Demonstrated value

The pilot was completed within two months, followed by work on a five-year rollout proposal.

04

Enterprise technical strategy · Paris

Technical leadership for a $20MM+ GACV opportunity

TotalEnergies and AspenTechExperience brought into Melo Energy from a prior AspenTech/Emerson role
Context

A strategic enterprise opportunity required alignment from executive need through technical scope.

Delivery contribution

Melo Energy's founder led the initial C-level engagement, coordinated global resources, and wrote the technical proposal.

Demonstrated value

The work connected executive sponsorship, technical requirements, and a defensible delivery case.

05

Applied R&D · Sand production and management

Real-time sand prediction and model calibration

PTTEP project environmentFounder prior-role experience and patented method
Context

Field teams needed a more dynamic method for evaluating erosional sand risk as operating conditions changed.

Delivery contribution

Melo Energy's founder directed field and numerical-simulation work using real-time data and engineering validation.

Demonstrated value

The method became part of a public U.S. patent for calibrating erosional sand prediction.

Public evidence

Verify the technical record at its source.

Public source documents and founder references provide supporting context without relying on unattributed website claims.

Apply the experience

Which part of the next assignment needs stronger technical ownership?

Share the technical scope, required role, timing, and delivery context through the assignment inquiry.