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Troinex, Switzerland

Walgenwitz A.,Ephesia Consult | Allard D.,French National Institute for Agricultural Research | Biver P.,Total S.A.
Petroleum Geostatistics 2015

The object-based simulation is revisited by handling objects with complex geometry to overcome the lack of realism of standard object-based simulation. GPGPU capabilities (General-Purpose Computing on Graphics Processing Units) are used to achieve the intensive numerical computations and intensive geometric queries. The case study presented to illustrate the technique is the modeling of the internal architecture of a lobe complex in a reservoir of Western Africa. Source

D'Or D.,Ephesia Consult | Destain M.-F.,University of Liege
Mathematical Geosciences

It is well known that soil compaction affects root growth and disrupts the activity of soil microfauna and microorganisms, resulting in yield loss. With the more intensive use of heavy machines in agriculture and forestry, the risk of soil compaction is increasing. In this study, precompression stress (Pc) was chosen as an indicator of the susceptibility of soils to compaction and was calculated using pedotransfer functions (PTFs). PTFs involve eight variables related to the hydraulic and mechanical behaviour of soils: organic matter content, bulk density, air capacity, available water capacity, non-plant available water capacity, saturated hydraulic conductivity, cohesion, and angle of internal friction. Combining these PTFs with geostatistics and Monte Carlo simulations, Pc maps were produced at the regional scale for Wallonia in Belgium, accompanied by uncertainty quantification maps. These maps were then used to produce compaction risk maps based on common scenarios. The results showed that the modal Pc map was coherent with the spatial distribution of the main variables, namely soil texture and organic matter content. The risk maps enabled areas with a compaction risk in both agricultural and forestry contexts to be identified. These maps could be useful in drawing up soil protection measures and policies. © 2015, International Association for Mathematical Geosciences. Source

D'Or D.,Ephesia Consult | Destain M.-F.,University of Liege
Soil and Tillage Research

The spatial analysis of the soil compaction risk has been developed at the regional level and applied to Wallonia (Belgium). The methodology is based on the estimation of the probability of exceeding the preconsolidation stress due to the application of loads on the soil.Preconsolidation stresses (Pc) are computed from the pedotransfer functions of Horn and Fleige (2003) at pF 1.8 and 2.5 and classified into 6 categories ranging from very low Pc (<30. kPa) to extremely high Pc (>150. kPa). The computation requires the knowledge of pedological (texture, organic content), mechanical (bulk density, cohesion, internal friction angle), and hydraulic variables (water content available, non-available water content, air capacity, saturated hydraulic conductivity). These variables are obtained from databases like HYPRES or AARDEWERK or from pedotransfer functions. The computation of Pc takes into account the spatial structure of the data: in some cases, data are abundant (e.g., texture data) and spatial variability is taken into account through geostatistical methods. In other cases, the data is sparse but uncertainty information can be extracted from the knowledge of the statistical distribution. Maps of the most probable Pc class are produced. Uncertainty is computed as the classification error probability. Implementation of these methods in Wallonia showed that Pc values higher than 120. kPa are reached either on 64% of the territory at pF 2.5 or on 55% at pF 1.8. A higher uncertainty was found at pF 2.5 than at pF 1.8. Uncertainty was also found higher for clay and clayed loess than for other textural classes present in Wallonia.The risk of compaction is defined as the probability that Pc is exceeded by the stress created by a load applied to the soil at a depth of 40. cm, the loads being similar to those induced by agricultural or forestry tires. It appeared that subsoil compaction risks exist mainly in loamy forest soils with small coarse fragments supporting loads similar to that existing on logging machines.In the zones where the uncertainty is low, the developed tool could be used as a basis for providing policy measures in order to promote soil-friendly farming and forest practices. © 2014 Elsevier B.V. Source

D'Or D.,Ephesia Consult | Braccini E.,Total S.A. | Biver P.,Total S.A.
Petroleum Geostatistics 2015

Among the process-based methods, the Lateral Offset Channels (LOSCs) simulation method has been especially designed to simulate logical chronological sequences of channels. The last (most recent) channel observed on spectral maps is fitted with a B-spline. To simulate the older channels in the sequence, the control points of the B-spline are moved along a parabolic path towards the fairway centerline, thus modifying the original B-spline in a logical way. In this paper, we particularly emphasize on the uncertainty on the fairway borders and show how actual borders corresponding to various scenarios can be drawn and how the channels simulation is adapted accordingly. In particular, we show on a field case that a larger fairway results in more meandering channels than a narrower one. All channels in a sequence are consistent with each other and can be conditioned to well data. Source

Allard D.,French National Institute for Agricultural Research | D'Or D.,Ephesia Consult | Froidevaux R.,Ephesia Consult
European Journal of Soil Science

We address the problem of the prediction of a spatial categorical variable by revisiting the maximum entropy approach. We first argue that, for predicting category probabilities, a maximum entropy approach is more natural than a least-squares approach, such as (co-)kriging of indicator functions. We then show that, knowing the categories observed at surrounding locations, the conditional probability of observing a category at a location obtained with a particular maximum entropy principle is a simple combination of sums and products of univariate and bivariate probabilities. This prediction equation can be used for categorical estimation or categorical simulation. We make connections to earlier work on prediction of categorical variables. On simulated data sets we show that our equation is a very good approximation to Bayesian maximum entropy (BME), while being orders of magnitude faster to compute. Our approach is then illustrated by using the celebrated Swiss Jura data set. © 2011 The Authors. Journal compilation © 2011 British Society of Soil Science. Source

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