Optimization of waterflooding performance by using finite volume-based flow diagnostics simulation

Abstract

From a visual point of view, volumetric information about reservoir portioning and communication such as sweep, flow patterns, and drainage zones are longer better interpreted and pictured when presented by an average volumetric flux calculation. To this hand, finite volume discretization can be used to substitute streamline simulation-based finite difference to assess flow diagnostic information. Herein, we use finite volume-based flow diagnostics to optimize waterflooding. In particular, we discretize in finite volume the flow equation from Darcy's law single-phase incompressible flow steady state combining with two auxiliary flow equations, time of flight and stationary tracers using the two-point flux approximation to describe fluid particles motion and flow lines. In addition, with the estimation of dynamic heterogeneity, we compute the Lorenz coefficient to highlight the reservoir flow and storage capacity characterization. To optimize waterflooding rates, we first, use an objective function the equalized Lorenz coefficient got through the evaluation of average travel time in cells to increase sweep efficiency and decrease the dynamic heterogeneity coefficient. Second, following the same target, we use the flow diagnostic interactive tools to study the volumetric sweep displacement front and harmonize the flooding breakthrough. In this work, our conceptual approach is to see the reservoir initially filled with oil; then, optimizing the Lorenz coefficient leads us to an oil recovery improvement. To be pragmatic, we apply our waterflooding performance optimization model on two case studies, the ninth SPE comparative solution project, a reexamination of black-oil (synthetic case) and ZHNBA Chinese oilfield (real field dataset).

Studies on natural gas reserves multi-cycle growth law in Sichuan Basin based on multi-peak identification and peak parameter prediction

“…The curve increases gently at first, then rises rapidly, then increases slowly and then reaches a peak and begins to decline in a symmetrical form. This model is more suitable for predicting the growth law of natural gas reserves with relatively gentle changes (Mfoubat and Zaky 2019;H Refsnes., Diaz M., Stanko M. 2019).…”

Production Forecasting at Natural Gas Wells

“…The Gauss model is better suited for predicting the growth pattern of natural gas reserves with gradual changes. This is because the peak time of the model is relatively late, and the curve tends to be wide and increases gradually at first, then rises rapidly, before increasing slowly and eventually reaching a peak and declining in a symmetrical form [27]. By modeling the normal distribution of key variables such as gas flow, this method accurately estimates the probabilities associated with each scenario, providing a more comprehensive view of possible fluctuations in production.…”

The use of flow diagnostics to rank model ensembles

“…Although using a simplified model means results are not quantitatively correct, the relative heterogeneity between models can still be estimated. Various types of flow diagnostics computed with finite-volume methods have been utilized to develop proxies that differentiate between macroscopic and microscopic sweep improvements resulting from polymer injection [13], to optimize waterflood performance [5,19,20], to validate rapid prototyping of reservoir models [9], in production data integration [23], to rank downscaled models in chemical EOR [35] or validate upscaling methods [16,22], and to cluster [36] or initialize [3] data-driven models, to mention a few applications.…”