Conclusions and perspectives

Résumé français: Deux sujets bien distincts ont été traités dans ce travail: le conditionnement de Flumy aux données de puits d'une part, la détermination des unités stratigraphiques par un algorithme de classification automatique appliqué aux données de puits d'autre part.

Les tests ont révélé que les principes généraux du conditionnement dynamique aux données de puits sont plutôt efficaces, mais que les algorithmes pouvaient conduire à des situations indésirables et devaient être améliorés. Le conditionnement dynamique est basé sur un principe d'attraction aux données de sable et de "répulsion" aux données d'argile. De façon générale cependant, il y avait un déséquilibre être les processus d'attraction, dominants, et ceux de répulsion, déséquilibre responsable d'un dépôt excessif de sable à l'endroit des puits comme à leur amont. Une attention spéciale a due être portée aux problèmes posés par la transition entre lithofaciès chenalisés et non chenalisés le long des puits, en particulier lorsque le chenal est présent sur le puits. La révision des algorithmes, notamment concernant la mise à jour du niveau dit actif en chaque puits, a permis une amélioration significative des résultats. L'obtention d'un conditionnement à 100% parait plus du ressort d'un "post-processing" permettant d'effacer les écarts entre données et simulation à l'endroit des puits et dans leur voisinage. Des pistes de conditionnement par des voies statistiques, plutôt que par des modifications portant sur les processus physiques, sont envisageables moyennant une puissance de calcul démultipliée.

La détermination des unités stratigraphiques est une étape préalable à la simulation de réservoirs. L'adaptation d'un outil de classification automatique hiérarchique géostatistique à la courbe de proportion verticale des puits fournit une aide à l'utilisateur pour l'identification des unités. Le développement d'un critère d'optimalité pourrait rendre la détermination des unités entièrement automatique. Actuellement l'outil exploite la courbe de proportion verticale globale du sable calculée sur les puits. Il pourrait être étendu à plus de variables. Une autre perspective pourrait être la détermination d'unités délimitées par des surfaces autre que des plans horizontaux.

CONCLUSIONS AND PERSPECTIVES

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5.1

General Discussion

Various types of models are used for the creation of natural reservoir numerical analogues. Flumy is a process-based model, with three main processes included into simulation: migration, aggradation and avulsions. Two types of conditioning to field data are possible, in order to create more realistic simulations: seismic data conditioning (soft) and well data conditioning (hard). This thesis discusses only conditioning to wells. Two main questions were considered during the work.

The first one concerns a procedure of conditioning and was presented in Chapters 2 and 3. Conditioning to wells in Flumy is a dynamic procedure – no trial / error, the well data are reproduced during the simulation. Well data are interpreted according to a lithofacies classification which divides the data lithofacies into three main classes: Channelized, Levee and Fine-Grained lithofacies. This classification is further used to adapt the main Flumy simulation processes for well reproduction. To do this, special conditioning techniques are applied: “repulsion” / “attraction” of the channel to the well location (increase / decrease of migration velocity in the direction of wells); adapted tossing of new channel paths in the appropriate locations by creating a “pseudo topography”; aggradation blocking in extreme cases.

Choice of simulation parameters which are consistent with the well data is an important issue of conditioning procedure: the more compatible the parameters are with the data, the less the conditioning is violent for the original Flumy simulation process. Now, a simulation can be run with three main input parameters: N_G (sand expected proportion in simulation), HMAX (maximal channel

depth) and ISBX (sandbodies extension coefficient). In this case, the other parameters can be

approximately defined by the Nexus tool, which is based on heuristic formulas. There exists a method of HMAX definition from the well data. Determination of ISBX is quite an issue as a computation of

horizontal parameter from vertical data. Definition of N_G from the wells is a simple task of sand proportion computation, but only if the modelled reservoir is vertically homogeneous in sand.

In the case when several stratigraphic layers are presented in reservoir, it should be modelled as a sequence of units with different simulation parameters. In Chapter 4, a new method of determination of such units from the well data is proposed. This method permits to evaluate a vertical heterogeneity of sand present in wells, and to define the horizontal units which are internally more homogeneous in terms of sand proportion. The user can visualize the difference in a chosen number of units and define the optimal units for simulation. The method is based on Geostatistical Hierarchical Clustering; as one of unsupervised learning methods, clustering returns results which can be better than a simple visual analysis.

The following two sections contain the more detailed conclusions that can be made on the two main points.

CONCLUSIONS AND PERSPECTIVES

5.1.1 Improved dynamic conditioning to well data

The review of various techniques, presented at the beginning of Chapter 2, shows the main hypothesis of well data conditioning: the sand deposits at well are associated with the channel presence at well; the non-sand deposits, in opposite, are associated with the channel path far from the well. In Flumy, the similar principle is used, but with a more realistic approach – the channel by itself is not equal to sand, but it stays the source of sand deposits: Points Bars, constructed during the channel migration; Crevasse Splays, constructed during levee breaches.

Conditioning to well data already existed in Flumy, before this thesis work. The first objective was therefore to evaluate its efficiency on various types of simulation scenarios (including simplistic cases). The most significant results of these tests were presented in Chapter 3.2 – 3.3. The initial conditioning procedure was analyzed using two scales: local (percentage of exact matching between data and simulation at well location) and regional (comparison of sand distribution in non-conditional and conditional simulation blocks with the same input parameters). The tests revealed that the main conditioning principles were rather efficient but not perfect, and could be improved. A list of problems to be fixed was noted, as a reference for the future modifications (Chapter 3.4). A special attention was paid to the transition between Non-Channelized and Channelized lithofacies in one well (switch between opposite conditioning techniques), and uniformity of sand distribution in conditional simulation blocks.

The modifications of the conditioning procedure were then performed and tested (Chapter 3.4 – 3.5). The main concepts stayed the same; the biggest change made is a completely reviewed Update AL algorithm. It deals more accurately with the channel at well (Wet Well), which helps to equilibrate “repulsion” / “attraction” techniques. Other conditioning algorithms were corrected, in order to reduce the previously observed undesirable impacts of conditioning: migration and avulsion adaptation became less violent, and aggradation blocking was restored. Improvement of results at both scales, local and regional, was proven and illustrated using the same tests as for Initial Conditioning evaluation (it permits to visualize the “net” differences in results).

With the new techniques, the proportion of excessively deposited Channelized facies is equal to 0-2 percentage points (compared with 3-13 presented in Initial conditioning tests), and the spatial sand distribution in conditional simulation blocks is visibly more uniform. One undesirable impact of conditioning, which can still be observed, is an insufficient (compared to the rest of simulation block) sand deposition in a small vicinity of wells. It is a result of the “repulsion” for Non-Channelized facies deposition and protection; cancelling this option makes the simulation even more uniform, but decreases significantly the exactness of well reproduction.