Technology Update: Mature Flood Surveillance Using Streamlines

Abstract

Technology Update With the recent drop in oil prices, operators are shifting to optimization of existing assets with minimal costs. For mature floods (water, chemical, and CO2), one low-cost optimization strategy is the intelligent adjustment of well-rate targets. While it is easy to identify high-water-cut or high-water-rate producers, it is not obvious to identify which injectors are contributing to oil production or fluid cycling. This makes setting injection targets a guessing game at best without a calibrated reservoir (simulation) model. However, detailed calibrated reservoir models require simulation expertise, are time-consuming to build, and can even be considered overkill for short-term reservoir management. Reservoir surveillance techniques sidestep this problem by using measured well data combined with simpler models to create a feedback loop that is informative and valuable for reservoir management. The starting point for any improvement of an ongoing flood is the proper identification of well patterns and reliable pattern metrics. Which patterns have historically outperformed and which have underperformed? How much oil is being recovered from each pattern for each unit of volume injected into the pattern? Being able to answer these questions with confidence enables target rates to be set that will improve sweep and reduce fluid cycling. And as new production/ injection data are collected, rate targets are realigned to ensure that field recovery remains close to optimum. Streamlines as a Solution Streamlines, which represent flow from injectors or aquifers to producers, offer a powerful solution to define injector patterns and associated key production metrics. Advances in streamline-based flow modeling since the early 1990s allow streamlines to be traced in 3D, account for complex geological descriptions, include all well geometries, and incorporate a wide range of flow physics. However, applying streamlines for surveillance requires only a subset of these extensions and is much simpler to implement. Most floods are driven by pressure gradients rather than absolute pressure, and at reservoir conditions, it can be assumed that the fluids are nearly incompressible. This is certainly true for water/polymer/ chemical floods. Even CO2 at high pressure behaves like a liquid. For surveillance purposes, the calculation for the total velocity field needed to trace the streamlines can be significantly simplified. Specifically, the velocity is solved conditioned to a) measured (historical) total injected and produced volumes at the wells; b) a description of the subsurface geology, including faults and flow barriers if available, and if not, a homogenous box can be used; and c) an assumption of in-situ fluid distributions if available, and if not, a uniform saturation distribution will do. Although these assumptions seem substantial, they are reasonable for surveillance because the primary objective is to identify current well pairs and allocation factors rather than forecasting. Model Calibration Not Needed The assumption of fluid incompressibility implies that the past spatial pressure distribution and gradients are immaterial to the solution of the current velocity field, and so streamlines can be extracted for any moment in time without the need to calibrate the model to the past. Using measured historical produced and injected total rates implies that the velocity field will properly reflect the influence of wells relative to each other in terms of production/injection volume and spatial location.