Streamline Simulation of Barium Sulfate Precipitation Occurring Within the Reservoir Coupled With Analyses of Observed Produced-Water-Chemistry Data To Aid Scale Management

Abstract

Summary In waterflooded reservoirs under active scale management, produced-water samples are routinely collected and analyzed, yielding information on the evolving variations in chemical composition. These produced-water chemical-composition data contain clues as to the fluid/fluid and fluid/rock interactions occurring in the subsurface, and are used to inform scale-management programs designed to minimize damage and enable improved recovery. In this interdisciplinary paper, the analyses of produced-water compositional data from the Miller Field are presented to investigate possible geochemical reactions taking place within the reservoir. The 1D and 2D theoretical model has been developed to test the modeling of barium sulfate precipitation implemented in the streamline simulator FrontSim. A completely 3D streamline simulation study for the Miller Field is presented to evaluate brine flow and mixing processes occurring in the reservoir by use of an available history-matched streamline reservoir-simulation model integrated with produced-water chemical data. Conservative natural tracers were added to the modeled injection water (IW), and then the displacement of IW and the behaviors of the produced water in two given production wells were studied further. In addition, the connectivity between producers and injectors was investigated on the basis of the comparison of production behavior calculated by the reservoir model with produced-water chemical data. Finally, a simplified model of barite-scale precipitation was included in the streamline simulation, and the calculation results with and without considering barite precipitation were compared with the observed produced-water chemical data. The streamline simulation model assumes scale deposition is possible everywhere in the formation, whereas, in reality, the near-production-well zones were generally protected by squeezed scale inhibitor, and, thus, the discrepancies between modeled and observed barium concentrations at these two given wells diagnose the effectiveness of the chemical treatments to prevent scale formation.

Comprehensive Evaluation of Offshore Oilfield Development Plans Based on Grey Clustering Analysis with Cloud Model

“…The global properties of a cloud can be presented by its digital features, which are Expected value ( ), (Entropy), and (Hyperentropy) [25]. Here, represents the uncertainty measure of qualitative concept, and represents the uncertainty measure of the .…”

“…Then we can obtain dimensionless data by normalizing the data, where different index types have different normalization methods. Meanwhile, according to the cloud model [25], the cloud of normalized data can be calculated by backward normal cloud generator, which the Algorithm 1 represented.…”

“…The conditional cloud generator is composed of the antecedent cloud generator and the consequent cloud generator, Figure 2. In this paper, we employ single condition rule cloud generator [25]. The details of the single condition single rule cloud generator are presented in Algorithm 2.…”

“…According to the above content, the characteristics of plans are transformed into the synthesized clouds in Section 2.4, which contains three characteristics: , , and . According to the cloud model [25], the bigger is, the better it is, and the smaller and are, the better they are. The tendency of numerical characteristic is different, so the reciprocal of and is used to replace their value.…”

“…Then there have been many improvements and applications related to the grey system, which are Fuzzy-TOPSIS [19,20], Principal Component Analysis with grey [21], and Grey-Relational Analysis Method [22]. However, the fuzziness or randomness of data is usually ignored in these methods and the determination of the index weights is not reasonable enough [23][24][25], which does not consider the relation between fuzziness and randomness or ignore the influence of human in engineering application. Meanwhile, alongside the increase of evaluation indexes, the accuracy of the evaluation scheme is getting lower and lower.…”