ABSTRACT
Chemical flooding using a polymer and/or surfactant has been widely applied in oilfields worldwide for enhanced oil recovery. Chemical adsorption in reservoirs has a significant effect on the rock permeability and wettability and hence can affect the overall oil production. In this work, two chemicals, namely, the xanthan gum (XG) biopolymer and sodium dodecylbenzenesulfonate (SDBS) anionic surfactant, were used individually as displacement fluids. The amount of chemical adsorption on the rock surface and the residual resistance factor (permeability reduction) were calculated throughout the flooding experiments using an unconsolidated sandstone (SS) pack model. The effects of the injected chemicals' concentration and reservoir salinity on adsorption capacity have been examined. Additionally, the effect of the addition of nanosilica particles (NSPs) to the injected fluid on the rock adsorption was also investigated. The results showed that the amount of XG and SDBS adsorption on the rock surface increased, albeit to a different extent, by increasing the chemical concentration at the applied salinities (0, 3.5, 5, and 10%) of the displacement fluids. Also, the permeability reduction increased with the increase in XG and SDBS concentrations; however, permeability reduction due to SDBS flooding was lower than that of XG in SS. The use of NSPs as a coinjectant to the XG and SDBS displacement fluids increased the adsorption on the SS rock. A plausible mechanism for the adsorption of the XG/NSP and SDBS/NSP blends on the SS surface was proposed. A density function theory calculation was employed to establish a relation between the adsorptivity of NSPs on SDBS and XG and the total energy and dipole moment of the molecules.
ABSTRACT
Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from non-being field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of -0.67, and an absolute average relative error of 3.18.
