Oilfield Technology - May/June 2020

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Oilfield Technology

May/June 2020

Studies are no longer limited by simulation size nor scale, but by the

ability to digest the generated data and infermeaningful insights given the

uncertainty; the bottleneck hasmoved.

According toHyperion Research, spending for HPCwork in the cloud

is expected to grow fromUS$2.5 billion spent in 2018 toUS$7.4 billion

in 2023.

Parallel reservoir simulators

The second reason for the improved capability of reservoir simulation

is the emergence of new parallel simulators that are able to employ the

advantages offered by themodern hardware. Although processes such

as seismic imaging are naturally amenable tomassive parallelism, it is

more challenging to expose such parallelism in reservoir simulation. It

tookmore time and effort to create parallel reservoir simulators, which

is evident in how companies have used their HPC systems over the

years. Historically, companies have dedicated themajority of computing

resources to the seismic imaging process, with reservoir simulation

being a distant second. This scenario is gradually changing as energy

companies begin to usemore parallel simulators andmove tomore

probabilistic methods of reservoir modelling.

The increase in computing power is changing howcompanies view

the use of reservoir simulation. Instead of using one or a fewmodels

to represent the reservoir, they aremoving towardsmore statistical

methods, such as ensemblemodelling. Ensemblemodelling is a technique

where thousands of different realisations of amodel are simulated to

provide an envelope of possible outcomeswith probabilisticweighting.

Ensemblemodelling recognises and embraces uncertainty, and provides

statistical bounds on future production. This enables companies to better

understand the uncertainty associatedwith the reservoir and avoidmore

ad-hoc assumptions during the decision-making process. It also assists the

machine learning and AI methods used by oil companies by creating the

large sets of data that are required. Methods such as ensemblemodelling

or uncertainty quantification require heavy computing power, which

has historically limited their use in traditional reservoir simulation. This

burden has nowbeenmitigated and companies such as Eni, with its new

GPU-basedHPC5 supercomputer, nowchoose the best development

scenario by creating ensembles of models and running hundreds to

thousands of simulations.

Companies are alsomoving towards developing andmodelling

larger, more fine-grainedmodels. Traditional reservoir simulation

involves the process of upscaling, where detail is removed from large

geological models to create smaller simulationmodels that are faster

andmoremanageable. Modern parallel reservoir simulators, such as the

GPU-based ECHELON, enable companies to dispense with the upscaling

process and instead quickly simulate the full geologic sizemodel.

Geologic complexity is a very important factor that controls long-term

recovery. Maintaining the complexity developed inmodern geologic

modelling tools can be very important for understanding and optimising

recovery. Simulators allow companies tomodel these large, complex

systems at speeds that enable the practical simulation of hundreds or

thousands of ensemble realisations. More detailed and higher resolution

models provide engineers andmanagers with additional critical

subsurface data that informs decision-making. This is true even for very

small companies, such as Denver-based iReservoir consulting, where

models of several million or more active cells have become routine

with the use of ECHELON on small workstations. In another example,

Houston-based Marathon Oil Co. uses ECHELON to runmodels with tens

of millions of cells in full-field simulations that includemultiple wells

with complex fracture geometry.

The performance of modern parallel reservoir simulators has also led

to an increased use of more complex problems, such as compositional

modelling. Compositional modelling allows engineers to track how

the chemical composition of the hydrocarbon changes throughout the

production process. This is important in cases, such as carbon dioxide

(CO

) flooding, where the changing composition of the hydrocarbon

mix can dramatically impact the recovery. This type of modelling is very

compute-intensive and thus requiresmuch longer run times than less

complex simulation runs. Because of this, engineers have historically

avoided compositional modellingwhen possible bymaking assumptions

and limiting themodel complexity. This adds to the uncertainty in the

model and negatively affects business decisions.

Conclusion

Forces on both the demand and the supply side have impacted the role

of reservoir simulation in the energy industry. On the demand side there

is a growing emphasis on ensemblemethods, largermodels andmore

complex physics. All three drive the need for fast, scalable simulation of the

type offered by simulators such as ECHELON. On the supply sidemulti-core

CPUs andGPUs have emerged asmature foundational platforms for

scientific computing. These new technologies generate critical information

for better decision-making and cost savings in an industrywhere even tiny

improvements in efficiency or production can provide huge rewards.

Figure 1.

V100GPU.

Figure 3.

Pricing evaluation.

Figure 2.

Analysis of an ensemble.