At Ava, we believe that geological uncertainty is best understood when subsurface professionals can combine high-quality data with robust, industry-standard scientific methodologies to generate multiple scenarios.

Over the next four months we are pleased to offer a total of five webinars, which will showcase how multi-scenario modelling can be intuitive and cost-effective, while at the same time providing better representation of reservoirs, better volume estimations and optimised well placement.

All our sessions are live and interactive. Not only do they offer a learning opportunity, but they also allow time for discussion and Q&A, consequently providing a networking opportunity too.  Visit our library page to watch past webinars on-demand.

10 September 2019, 10:00AM & 4:00PM

Water Saturation uncertainty and reservoir models

Water Saturation Uncertainty and Reservoir Models

Water saturation is a key component to the hydrocarbon volume equation, and as such, uncertainties in its determination at log scale and application to reservoir models via saturation height functions should be investigated, and multiple or alternate scenarios considered.

In this webinar, we will look at the ways water saturation is calculated at the well log scale, and also how saturations are applied in 3D space. We will explore the uncertainties that can occur, and how these affect the calculation of hydrocarbon volumes.

24 September 2019, 10:00AM & 4:00PM
Improving Reservoir Facies Modelling with Analogue Databases

Improving Reservoir Facies Modelling with Analogue Databases: Fluvial examples from the Crux Field, NW Australia

Using analogue data to build or validate facies models is considered best practice in reservoir modelling. However, the practical reality of analogue usage is challenging. Quantitative analogue data distributions can be used to inform both object-based and variogram-based modelling algorithms. By incorporating appropriate analogue information, an improved and defendable distribution of reservoir facies through the model can be achieved.

15 October 2019, 10:00AM & 4:00PM
The structural uncertainty and reservoir models webinar is presented by Dan Hemingway

Structural Uncertainty and Reservoir Models

Faults are longer than interpreted due to seismic image limitations. How much longer is uncertain, as well as how that affects reservoir compartmentalisation. This webinar will show how that uncertainty and impact may be derived from your interpretation directly, how to generate alternative plausible fault networks, and how to quickly create correspondingly faulted reservoir models with them.

12 November 2019, 10:00AM & 4:00PM
Viki O’Connor presents a webinar about How quality data blended with scientific methodologies impacts decision making

How Quality data blended with scientific methodologies leads to increased confidence in volume ranges

This webinar will investigate how blending high-quality data available with proven scientific methodologies increases confidence in volume ranges. Ava Clastics will be used to generate multiple 3D facies realisations, anchored in analogue data and honouring the hard data at the well locations. We will then apply a variety of approaches and input data configurations to generate 3D water saturation properties using Ava Saturation. Finally, we will use the output from both to calculate more realistic volume ranges.

10 December 2019, 10:00AM & 4:00PM

Assessing and Quantifying Structural and Water Saturation Uncertainties in Reservoir Models

This webinar will investigate the effect fault connectivity has on reservoirs by way of the number of faults blocks or their area and the likely well count required in each of the scenarios. The uncertainties in the structure and the saturation models are viewed through multiple grids each with differing views on fault interaction and through varying the saturation calculation methodology. By quickly generating multiple structural grids and easily applying a range of saturation height functions to those grids, the uncertainty space can be explored.