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Is material balance analysis kryptonite for the modern geocellular model?

In an industry so driven by technology as oil and gas, new advances can have a major impact on how we work in our field. When it comes to matters like assurance, the trend today is to take advantage of computing power and create multi-million cell geological models, rich with imagined detail.

There’s a clear reason for this. Large simulation models allow geologists to present what they believe to be happening, and they reassure decision makers when you only have a few exploration wells in one small area of a field.

But just because today’s geocellular models can provide a thousand times more cells than was possible decades ago, does that mean the process is a thousand times better? And are there older, tried and tested methods that offer just as much, if not greater, accuracy – methods like material balance analysis theory?


A perfect model doesn’t always translate to perfect results

When dealing with something like a multi-million cell simulation, there’s one important thing to keep in mind: that greater detail doesn’t always equal greater accuracy. That’s particularly true when those details are based on interpretations, as is usually the case with geocellular models.

When you only have data from a few well bores to draw from, that only gives you a few feet of direct physical evidence, but the whole reservoir represents several kilometres of distance. With a potentially high variability of rock and fluid properties to take into account, every grid cell away from the well bore carries a wide margin of error.

This is where material balance analysis comes in. With the material balance equation, it’s possible to estimate the oil or gas initially in place in a field by taking samples of the fluid properties, and measuring the reservoir pressure and oil, gas and water production. Because material balance is based on direct measurements from the reservoir, rather than interpolations and averages applied to the entire field, it can give results with a high level of confidence, especially if there has been a significant amount of production.

Geomodels differ

At Rockflow, we’ve recently seen some instances of a large oil field and a large gas field attempting to calculate the oil and gas in place with both material balance and a geocellular model. In both cases, the STOIIP and GIIP calculated by the geomodels differed significantly from what the material balance expected.

The problem lay with the interpretations within the geocellular model, and the fact that several outliers in the data had been worked into the simulation when they should have been set aside. Material balance, on the other hand, is based on multiple, repeatable measurements and any outliers can be identified and removed from the data set, rather than accounted for by an increasingly complex geocellular model.


When to use the material balance equation and when not to

While material balance can provide a high level of confidence, it would be wrong to call it a silver bullet for all scenarios. For a start, it can only be used when an oil or gas field is already in production. It also produces less reliable results for tight reservoirs with isolated compartments or little permeability, or when less than around 10% of the in-place volume has been produced to give you data.

If you have no production data, you will have no change in reservoir pressure to measure. In this case, you will need to turn to the volumetric method – multiplying the reservoir’s area, thickness, porosity and oil and gas saturation by the oil or gas expansion factor – and right here is where geocellular models shine.

The results of a geocellular model using the volumetric method certainly aren’t guesswork. There is still a wealth of information that must be gathered and evaluated to calculate values for each cell – from interpreting seismic data and carrying out depth conversions, to taking fluid samples and expansion measurements, to decoding what wireline logs and core data say about porosity and oil/gas saturation.

However, it’s crucial to remember that this method doesn’t give you a definitive single answer for the oil or gas in place. Even with the best interpretations of the data, a geocellular model with no production data to work with can only give a reasonable range of STOIIP or GIIP in the reservoir.

But when you do have production data from a field, material balance can give you a reliable way to estimate the oil in place without relying on interpretation and extrapolation. When we produce oil from a field, the change in pressure is physics’ way of telling us what we need to know without the need to make any assumptions, and we should listen to that data whenever possible.


Is material balance still relevant in a geocellular world?

The simple answer is yes. Material balance analysis theory might have been around since the 1930s, but that doesn’t mean it’s outdated. It is after all just an equation, one that works just as effectively when calculated with a computer as with pen and paper.

It’s also important to note that this isn’t a question of old vs new, or of material balance vs computer models. The volumetric method isn’t the only process a geocellular model can follow – a multi-million cell simulation model applies material balance at every step to every cell. What matters most is that the principles of material balance are followed, and that we aren’t so driven by the need for perfect models that we distort them to make every datapoint fit.

Ultimately, material balance is one of the many tools in our arsenal when it comes to assurance. While a multi-million cell simulation can offer a reassuring degree of detail, material balance can help to investigate potential biases and root out assumptions that might lead a field evaluation astray.

To learn more about how Rockflow can help with matters of assurance and evaluation, read our article on ensuring reliability and consistency in quality assurance, or head over to our Technical Assurance page.

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