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In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel ‘smart gas’ concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector.
In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel ‘smart gas’ concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector.
Summary Understanding how pressure propagates in a reservoir is fundamental to the interpretation of pressure and rate transient measurements at a well. Unconventional reservoirs provide unique technical challenges as the simple geometries and flow regimes [wellbore storage (WBS) and radial, linear, spherical, and boundary-dominated flow] applied in well test analysis are now replaced by nonideal flow patterns due to complex multistage fracture completions, nonplanar fractures, and the interaction of flow with the reservoir heterogeneity. In this paper, we introduce an asymptotic solution technique for the diffusivity equation applied to pressure transient analysis (PTA), in which the 3D depletion geometry is mapped to an equivalent 1D streamtube. This allows the potentially complex pressure depletion geometry within the reservoir to be treated as the primary unknown in an interpretation, compared with the usual method of interpretation in which the depletion geometry is assumed and parameters of the formation and well are the unknown properties. The construction is based upon the solution to the Eikonal equation, derived from the diffusivity equation in heterogeneous reservoirs. We develop a Green’s function that provides analytic solutions to the pressure transient equations for which the geometry of the flow pattern is abstracted from the transient solution. The analytic formulation provides an explicit solution for many well test pressure transient characteristics such as the well test semi-log pressure derivative (WTD), the depth of investigation (DOI), and the stabilized zone (SZ) (or dynamic drainage area), with new definitions for the limit of detectability (LOD), the transient drainage volume, and the pseudosteady-state (PSS) limit. Generalizations of the Green’s function approach to bounded reservoirs are possible (Wang et al. 2017) but are beyond the scope of the current study. We validate our approach against well-known PTA solutions solved using the Laplace transform, including pressure transients with WBS and skin. Our study concludes with a discussion of applications to unconventional reservoir performance analysis for which reference solutions do not otherwise exist.
In unconventional reservoirs, well interference caused by hydraulic fracturing of a child well can significantly affect productivity of the parent well, leading to prolonged shut-ins and increased operational costs. Therefore, a reliable approach is needed to detect well interference, quantify impact, and forecast the resulting production profile. This study proposes a physics-based data-driven method for identifying well interference events and quantifying their effects on subsequent well production using minimal and easily accessible input data. In this work, we suggest a workflow for detecting well interference and quantifying its impact based on a transient well performance (TWP) methodology. The main steps in the workflow include computing the dynamic drainage volume (DDV) using the diffusive time of flight (DTOF) method, history matching on pressure and rates, calculating transient productivity index (PI), detecting breakpoints in PI evolution, and fitting a multi-segment TWP model to forecast production rates. We also implement physically consistent validation criteria based on directional variations in water-cut and pressure to determine if the anomaly is the result of well interference. The method is scalable and is run on a routine basis (daily) to pick up new events and automatically update the forecast for all wells in the field. The improved forecast is a result of two major effects – first, accounting for pressure variations as part of the TWP method that normalizes operational variations; and second, the ability to account for interference and extraneous influence to recalibrate well performance. We verified and validated the proposed method on multiple US onshore fields with extremely high accuracy. Both before and after well interference, the production forecast results were compared with actual production rates, and the results were highly accurate. Further, a root cause analysis was performed to find correlations between the interference results and potential driving factors. Our results indicate that not all interference events are detrimental to long term well performance. Some of the results generated by this method could be useful in understanding the mechanisms to predict or control future interference events. This study offers a systematic and computationally robust approach to identifying well interference events and quantifying their effects on subsequent well production using minimal and easily accessible input data. The proposed method provides a quick, distinct, and understandable physics-based data-driven approach that can be used as part of closed-loop reservoir management to scale up to the entire field.
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