loopUI-0.1: uncertainty indicators to support needs and practices in 3D geological modelling uncertainty quantification

To support the needs of practitioners regarding 3D geological modelling and uncertainty quantification in the field, in particular from the mining industry, we propose a Python package called loopUI-0.1 that provides a set of local and global indicators to measure uncertainty and features dissimilarities among an ensemble of voxet models. Results are presented of a survey launched among practitioners in the mineral industry, enquiring about their modelling and uncertainty quantification practice and needs. It reveals that practitioners acknowledge the importance of uncertainty quantification even if they do not perform it. Four main factors preventing practitioners to perform uncertainty quantification were identified: lack of data uncertainty quantification, (computing) time requirement to generate one model, poor tracking of assumptions and interpretations, relative complexity of uncertainty quantification. The paper reviews and proposes solutions to alleviate these issues. Elements of an answer to these problems are already provided in the special issue hosting this paper and more are expected to come.

Rapid Reservoir Modelling: Sketch-Based Geological Modelling with Fast Flow Diagnostics

Rapid Reservoir Modelling (RRM) is a software tool that combines geological operators and a flow diagnostics module with sketch-based interface and modelling technology. The geological operators account for all interactions of stratigraphic surfaces and ensure that the resulting 3D models are stratigraphically valid. The geological operators allow users to sketch in any order, from oldest to youngest, from large to small, or free of any prescribed order, depending on data-driven or concept-driven uncertainty in interpretation. Flow diagnostics assessment of the sketched models enforces the link between geological interpretation and flow behaviour without using time-consuming and computationally expensive workflows. Output of RRM models includes static measures of facies architecture, flow diagnostics and model elements that can be exported to industry-standard software. A deep-water case is presented to show how assessing the impact of different scenarios at a prototyping stage allows users to make informed decisions about subsequent modelling efforts and approaches. Furthermore, RRM provides a valuable method for training or to develop geological interpretation skills, in front of an outcrop or directly on subsurface data.

Spatial distribution of the geochemical associations in the Babyak Mo-Ag-Au-W-Bi-base metal deposit, Western Rhodopes (Bulgaria): application of factor analysis

A new approach combining geochemical and mineralogical studies with statistical methods in the Babyak Mo-Ag-Au-W-Bi-base metal deposit is presented. The data are used for 3D geological modelling of the deposit and help to better understand the zonation of the elements and the complex geological evolution of the deposit.

The application of VOI method to shape a strategy of additional exploration

Background. An optimal exploration strategy creates a significant share in value of project on exploration stage. The paper describes an example of solving the following tasks: determining the feasibility of additional exploration drilling; evaluating the value of drilling of one or more exploration wells; determining the optimal placement for exploration wells and drilling order. Authors presenting the modification of VoI (Value of Information) method and its application. Materials and methods. Complex probabilistic models were created summarizing main uncertainties and limitations, both geological, technical and technological. At the first stage three equiprobable geological concepts were made. For each concept probabilistic geological modelling was proceeded and then realizations corresponding to values of reserves P10, P50, and P90 were selected. Further, detailed production forecasts and economic estimates were performed. The analysis used the well pad and the corresponding area for exploration drilling as a unit of calculation. In the article the authors introduced the concept of remaining uncertainty. Application of modified VoI method allowed to form ‘dynamic’ (i.e. depending on exploration wells drilling order) range of areas for additional exploration which provide the best decrease of remaining uncertainty. An additional exploration strategy has been formed, which includes the necessary and sufficient number of wells and their drilling order. A decision tree was created depending on the success or failure of each subsequent exploration well. Results. The use of the modified VoI approach made it possible to achieve the objectives and obtain economical estimates, all of which combined to facilitate the adoption of decisions. As a result, a program for two exploration well drilling was created which would reduce the uncertainty by 90% from its initial value. Conclusions: The adopted VoI method could be applied to fields at the stage of additional exploration as well as to fields at early exploration stage to develop an exploration drilling strategy.

The influence of layer and voxel geological modelling strategy on groundwater modelling results

Reliable groundwater model predictions are dependent on representative models of the geological environment, which can be modelled using several different techniques. In order to inform the choice of the geological modelling technique, the differences between a layer modelling approach and a voxel modelling approach were analyzed. The layer model consist of stratigraphically ordered surfaces, while the voxel model consist of a structured mesh of volumetric pixels. Groundwater models based on the two models were developed to investigate their impact on groundwater model predictions. The study was conducted in the relatively data-dense area Egebjerg, Denmark, where both a layer model and a voxel model has been developed based on the same data and geological conceptualization. The characteristics of the two methodologies for developing the geological models were shown to have a direct impact on the resulting models. The differences between the layer and the voxel models were however shown to be diverse and not related to larger conceptual elements with few exceptions. The analysis showed that the geological modelling approaches had an influence on preferred parameter values and thereby groundwater model predictions of hydraulic head, groundwater budget terms and particle tracking results. A significance test taking into account the predictive distributions showed that for many predictions the differences between the models were significant. The results suggest that the geological modelling strategy has an influence on groundwater model predictions even if based on the same geological conceptualization.

LoopStructural 1.0: time-aware geological modelling

In this contribution we introduce LoopStructural, a new open-source 3D geological modelling Python package (http://www.github.com/Loop3d/LoopStructural, last access: 15 June 2021). LoopStructural provides a generic API for 3D geological modelling applications harnessing the core Python scientific libraries pandas, numpy and scipy. Six different interpolation algorithms, including three discrete interpolators and 3 polynomial trend interpolators, can be used from the same model design. This means that different interpolation algorithms can be mixed and matched within a geological model allowing for different geological objects, e.g. different conformable foliations, fault surfaces and unconformities to be modelled using different algorithms. Geological features are incorporated into the model using a time-aware approach, where the most recent features are modelled first and used to constrain the geometries of the older features. For example, we use a fault frame for characterising the geometry of the fault surface and apply each fault sequentially to the faulted surfaces. In this contribution we use LoopStructural to produce synthetic proof of concepts models and a 86 km × 52 km model of the Flinders Ranges in South Australia using map2loop.