Oil And Gas Plans Break When Reality Loops Back. GERT Was Built For That

Oil and gas projects rarely move in a straight line. A well plan changes after new data. A permit shifts a schedule. Weather closes a window. A supplier slips a delivery. The work still has to move, but the plan often cannot keep up.

That gap shows up in overruns and rework. In one major review of upstream oil and gas megaprojects, about 80% faced either cost overruns or schedule delays.  Those are big numbers, but they track what project teams see on the ground. Plans get written as if every task happens once, in one sequence. Real projects do not behave that way.

This is where project management tools can help or hurt. Many systems are great at tracking tasks. Fewer are good at modeling uncertainty, probability, and repeat work. That matters in oil and gas, where a “false start” can be a normal outcome, not a surprise.

In this article, we explain why traditional network methods struggle with real-world loops. Then we explain how GERT networks handle uncertainty more naturally. Finally, we show how Meliorem7 applies that approach in a cloud platform built for oil and gas delivery.

Why Oil And Gas Projects Rarely Follow The Baseline

A baseline schedule assumes the future will cooperate. In oil and gas, it often does not.

Geology is one reason. Subsurface data is never perfect. A drilling plan can change after new readings. Field development can shift after production behavior surprises the model. Those changes are not “bad management.” They are part of working with physical uncertainty.

Then there is logistics. Many projects depend on long lead items, specialized labor, and tight seasonal access. When one element slips, teams reroute work. They also repeat steps. A test fails. A seal needs rework. A QA issue sends a package back. The network becomes a loop.

This is not limited to small jobs. Large oil and gas projects have a long record of overruns and delays. EY’s review of upstream megaproject performance reported that a large share ran late or over budget, and it described common drivers across regions and project types.

The cost of that volatility is not only money. It is decision speed. When leaders do not trust the plan, they stop using it for decisions. They use it for reporting. The project becomes reactive, even when the team has good people and good data.

That is the core planning problem we built Meliorem7 to address. Oil and gas teams need a plan that can absorb uncertainty without constant manual rebuilds.

Where PERT And CPM Fall Short When Rework Is Normal

Traditional network methods are useful, but they come with assumptions.

CPM is typically taught with deterministic activity durations. PERT introduces probabilistic time estimates, but both are commonly applied in ways that still favor a fixed logic path. A standard explanation of the two methods notes that CPM assumes deterministic time estimates, while PERT treats time probabilistically.

The practical issue is not the math. It is the structure.

Many project networks assume that predecessor tasks must finish before a successor can start, and that tasks do not repeat. If a repeat is needed, teams often handle it through change requests, replanning, and manual updates. That works on paper. It can break down in fast-moving delivery, especially when repeat work is an expected outcome, not an exception.

Oil and gas also suffers from another common pattern. Risk analysis is often separated from the schedule network. A risk register sits in one system. The baseline lives in another. The team talks about risk, but the plan still reads like certainty.

When the project starts drifting, the baseline becomes something to defend. Teams spend hours explaining variance instead of modeling it. That is how cost and schedule variance becomes a constant state.

We take a different view. We treat uncertainty as a design input for the network itself, not an afterthought.

How GERT Networks Model Probability, Branching, And Repeat Work

GERT stands for Graphical Evaluation and Review Technique. It is a network analysis method designed for systems where outcomes are probabilistic and paths can branch or loop.

GERT is often described as useful when projects include false starts, repeated activities, and multiple outcomes. That is a common reality in complex technical work.

Instead of forcing every project into one deterministic sequence, GERT allows a network to represent:

• alternative paths with different probabilities

• activities that may repeat based on outcomes

• durations that vary and can be modeled stochastically

This is where simulation becomes practical. Monte Carlo simulation can run many network scenarios to show likely ranges, not just single-point dates.

Meliorem7 brings that approach into a cloud-based project management platform built for oil and gas delivery.  We generate and maintain a dynamic model of the project network on an ongoing basis, rather than treating the baseline as static.

Here is what that looks like in daily work:

• Multivariate network and budget modeling that accounts for probability and impact, not just a single path.

• On-demand variance modeling so teams can test scenarios without rebuilding the whole plan.

• Template WBS structures for the oil well lifecycle, which speeds setup and keeps plans consistent across wells and fields.

• Data-driven updates that reflect recent performance data and can incorporate technical inputs such as geological studies.

The point is not to make planning more complicated. The point is to make it more honest. When a plan can represent rework and branching, teams spend less time pretending the path is fixed.

A Baseline That Moves With The Project Changes Decisions

Oil and gas projects will always face uncertainty. The difference is whether the plan acknowledges it.

When the network can represent probability and repeat work, variance becomes something you model early, not something you explain late. That shift matters in budgeting, staffing, procurement timing, and field execution. It also matters in stakeholder trust.

We built Meliorem7 for teams that live with real project uncertainty, especially in oil and gas.  If your current tool tracks tasks well but struggles with risk, rework, and dynamic baselines, it may be time to look at a different planning model.

The simplest test is this: when the project loops back, does your plan break, or does it adapt?