Speed and Robustness: Two Benefits of Using Geological Analogues in Static Modelling

31.5 hours saved and improved representation of a fluvial reservoir by incorporating analogue data from the FAKTS database and Ava Clastics GeoCypherTM workflow


With a critical shortage of personnel and an expertise gap predicted in the future, much reservoir and facies modelling work could be left to early and mid-career geologists. Additionally, efficiency in the reservoir modelling workflow will need to be achieved in order to satisfy increased demand for field development plans. Quality might suffer under these two conditions: less experienced geologists and improvements in time to knowledge.


Modify the existing facies modelling workflow to incorporate expertise (by codifying knowledge packaged into technology) and utilise analogues to improve the robustness of facies models by grounding them in geological reality.


A mid-career geologist was able to build two facies models validated against 100s of analogues in 20.5 hours by using Ava Clastics® analogue database and sedimentology software. In contrast, the same geologist was able to produce only one model in 52 hours using an alternative method.


Creating a representative facies model is often challenging, for several reasons: a lack of well data describing the reservoir, the scarcity of analogue data used to build a model or even limited experience in using the modelling algorithms to build the facies model. Most asset teams rely on the expertise of senior geologists with years of experience in a specific field to create realistic facies models. In some cases, the organisation may have an in-house reference database of rocktypes and geological analogues, but for many large and mid-size E&P operators, these databases are not robust or globally accessible, and in some cases they don’t exist.
In today’s environment of tighter budgets and breakeven development costs that are higher than the price per barrel, accounting for reservoir heterogeneity at the facies scale can help operators more reliably understand how the rock units contribute to production. This understanding gives valuable support to investment related decisions such as which assets to retain, which to farm out, and whether to delay development or provide input to the development plan itself.
If the price of oil remains in the $40-60/bbl range over the next three to five years, the pressure to re-evaluate asset portfolios will increase as oil companies seek opportunities to remain competitive. Organisations that opted to delay field development (expecting a market recovery sooner than 2020) will be faced with a new problem. In order to maintain acreage they will be required to meet their field development obligations which places enormous time-pressure on the asset teams responsible for evaluating reservoir potential and production scenarios.
With a critical shortage of personnel and an increasing expertise gap predicted in the future, much of the facies modelling work could be left to early and mid-career geologists. Additionally, efficiency in the reservoir modelling workflow will need to be achieved in order to satisfy demand for reservoir models. With these two conditions, less experienced geologists and productivity improvements in time-to-knowledge, the quality of reservoir models could suffer.
In the following study, we aimed to answer this primary question: Can the facies modelling workflow be improved in terms of efficiency while maintaining or improving quality to help operators prepare for a future of limited expertise?
The goal of the timed-trial was to create a real-life scenario that E&P operators may face in the near-future as the industry’s seasoned geologists begin to retire, or simply decide not to return to the industry. To understand how an early- to mid-career geologist might go about creating a new reservoir model for an unfamiliar field, we invited a geologist with 12 years’ experience working in field development across several oil companies (IOC and independent) to utilize a dataset from the Crux Field consisting of 5 wells (4 with reservoir penetrations), full log suites for each well, drilling reports, checkshot data and 270 km2 3D seismic data to generate a new facies model from scratch. The geologist had no prior experience or knowledge of the Crux Field.
From this dataset, the participant was asked to create an analogue-based reservoir model in Petrel* E&P software platform under two different scenarios, both of which would be timed. The comparison sought to understand the differences between the two workflows in terms of time and model robustness.
In the first scenario, the subject was provided only the dataset and a computer with access to the Internet and a license of Petrel installed. In the second scenario, the subject was provided the exact same dataset and computer, only this time the participant had access to Ava Clastics and was asked to utilise the cloud-based application to perform their work. In both scenarios, the geologist was asked to document the process.


Methodology Phase 1 Phase 2 Phase 3 Total Number of Models Produced
Ava Clastics
4 hours
15 hours
1.5 hours
20.5 hours
Standard Practice
4 hours
36 hours
16 hours
52 hours

Utilising Ava Clastics, the geologist was able to quickly gain relevant analogue data, automatically quantifying the dimensions of the facies objects in the Depositional Concept for the study area. This saves the time required to source the relevant papers and extract the required information for building an object based facies model. Also the time taken to perform variogram analysis is drastically reduced using Ava Clastics. Ava Clastics uses analogue data from the FAKTS database, together with published geostatistical methods (Ritzi 2001) to inform the variogram ranges, meaning that the process of selecting an appropriate analogue not only populates the parameters for object modelling, but the variograms as well.
In addition, information regarding wavelength and amplitude was also available when using the analogue database. This helped the geologist create a more robust model, because without this information from Ava Clastics, the Petrel defaults would have be applied due to the difficulty sourcing the required data. The downside of using these standard defaults is that their origin is unknown and their appropriateness to the model can be questioned. When a geologist is asked to defend their decisions, using defaults is a less reliable answer than citing the research, technical papers and resources backing their data input. The audit trail captured in Ava Clastics helps the team understand the origin of the model and the changes over time.
*Mark of Schlumberger
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Case Study (Extended Version)

Speed and Robustness: Two Benefits of Using Geological Analogues in Static Modelling
In this extended version of the case study, you’ll see images of the reservoir models, you’ll have access to the full details of the timed-trials, and you’ll be able to see the robustness of the analogues as plots produced in Ava Clastics.
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Each to their Own - the bias of analogues

In this whitepaper you will see outcome of what happened when several geologists were asked to interpret between gamma ray and sedimentary logs (Fig. 3b). The panel contains two logs over a 150m section.

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Experience the time savings and modelling robustness of Ava Clastics firsthand! We’ve established an online portal where, for a limited time, you can utilise Ava Clastics to generate analogue-informed reservoir models.