SPE Journal最新文献

To address the significant scaling challenges within the near-wellbore formation of ultradeep natural gas reservoirs characterized by high temperature and high salinity, we developed a dynamic scaling prediction model. This model is specifically designed for the prediction of scaling in gas-water two-phase seepage within fractured-matrix dual-porosity reservoirs. It accounts for the concentration effects resulting from the evaporation of water on formation water ions. Our scaling model is discretely solved using the finite volume method. We also conducted on-site dynamic scaling simulations for gas wells, allowing us to precisely predict the distribution of ion concentrations in the reservoir, as well as changes in porosity and permeability properties, and the scaling law dynamics. The simulation results reveal a significant drop in formation pressure, decreasing from 105 MPa to 76.7 MPa after 7.5 years of production. The near-wellbore formation is particularly affected by severe scaling, mainly attributed to the radial pressure drop funneling effect, leading to a reduction in scaling ion concentrations in the vicinity of the wellbore. Calcium carbonate is identified as the predominant scaling component within the reservoir, while calcium sulfate serves as a secondary contributor, together accounting for roughly 85.2% of the total scaling deposits. In contrast, the scaling impact on the matrix system within the reservoir remains minimal. However, the central fracture system exhibits notable damage, with reductions of 71.2% in porosity and 59.8% in permeability. The fracture system within a 5-m radius around the wellbore is recognized as the primary area of scaling damage in the reservoir. The use of the simulation approach proposed in this study can offer valuable support for analyzing the dynamic scaling patterns in gasfield reservoirs and optimizing scaling mitigation processes.

Operators often require real-time measurement of fluid flow rates in each well of their fields, which allows better control of production. However, petroleum is a complex multiphase mixture composed of water, gas, oil, and other sediments, which makes its flow challenging to measure and monitor. A critical issue is how the liquid component interacts with the gaseous phase, also known as the flow pattern. For example, sometimes liquids can accumulate in the lower part of the pipeline and block the flow completely, causing a gas pressure buildup that can lead to unstable flow regimes or even accidents (blowouts). On the other hand, some flow patterns can also facilitate sediment deposition, leading to obstructions and reduced production. Thus, this work aims to show that deep neural networks can act as a virtual flowmeter (VFM) using only a history of production, pressure, and temperature telemetry, accurately estimating the flow of all fluids in real time. In addition, these networks can also use the same input data to detect and recognize flow patterns that can harm the regular operation of the wells, allowing greater control without requiring additional costs or the installation of any new equipment. To demonstrate the feasibility of this approach and provide data to train the neural networks, a water-air loop was constructed to resemble an oil well. This setup featured inclined and vertical transparent pipes to generate and observe different flow patterns and sensors to record temperature, pressure, and volumetric flow rates. The results show that deep neural networks achieved up to 98% accuracy in flow pattern prediction and 1% mean absolute prediction error (MAPE) in flow rates, highlighting the capability of this technique to provide crucial insights into the behavior of multiphase flow in risers and pipelines.

Sustained injection of industrial-scale volumes of cold CO₂ into warmer subsurface rock will result in extensive cooling which can alter rock mass mechanical behavior and fluid migration characteristics. Advanced simulation tools are available to assess and characterize such phenomena; however, the effective use of these tools requires appropriate injection temperatures and rock thermophysical parameters (in addition to geomechanical and hydraulic properties). The primary objective of this study was to demonstrate the sensitivity of injection-induced tensile fracturing and fault reactivation to injection temperature and reservoir thermophysical properties during CO₂ injection operations. This was achieved by (1) compiling and reviewing thermophysical parameter data available for formations in the province of Alberta, Canada, and CO₂ injection temperature records for CO₂ injection projects in western Canada and (2) using a 3D, physics-based, fully integrated hydraulic fracturing and reservoir simulation numerical model to examine the geomechanical response of several potential CO₂ reservoirs in the Alberta Basin as a function of injection temperature, thermal conductivity (TC), and coefficient of linear thermal expansion (CLTE) values. The simulation results indicate that reducing the fluid injection temperature from 15°C (assumed in previous work) to 2°C (conservative value selected based on temperature data reviewed in this work) could trigger extensive vertical (20–130 m high, 100–600 m long) tensile fractures with rapid fracture initiation and full vertical growth within short periods (weeks to months) and continued horizontal length increase. When low values for thermophysical properties are used, the results show that thermally-induced tensile fracturing is unlikely, whereas the use of high values results in extensive tensile fracturing in all simulations. A similar conclusion was reached for the thermally-induced reactivation (unclamping) of proximal, critically-stressed faults. Notably, slip is predicted for all simulations where high thermophysical property values are used. This confirms that accurate determination of minimum fluid injection temperature and thermophysical parameters is important for containment risk assessment for commercial-scale CO₂ storage projects. Another significant outcome of this work is the observation that most thermophysical parameters in the available data were measured using experimental conditions and/or temperature paths that are not representative of CO₂ injection projects. As such, the development and validation of best practice approaches for accurate assessment of these parameters seem necessary.

Injecting CO₂ into reservoirs for storage and enhanced oil recovery (EOR) is a practical and cost-effective strategy for reducing carbon emissions. Commonly, CO₂-rich industrial waste gas is used as the CO₂ source, whereas contaminants such as H₂S may severely impact carbon storage and EOR via competitive adsorption. Hence, the adsorption behavior of CH₄, CO₂, and H₂S in calcite (CaCO₃) micropores and the impact of H₂S on CO₂ sequestration and methane recovery are specifically investigated. The Grand Canonical Monte Carlo (GCMC) simulations were applied to study the adsorption characteristics of pure CO₂, CH₄, and H₂S, and their multicomponent mixtures were also investigated in CaCO₃ nanopores to reveal the impact of H₂S on CO₂ storage. The effects of pressure (0–20 MPa), temperature (293.15–383.15 K), pore width, buried depth, and gas mole fraction on the adsorption behaviors are simulated. Molecular dynamics (MD) simulations were performed to explore the diffusion characteristics of the three gases and their mixes. The amount of adsorbed CH₄, CO₂, and H₂S enhances with rising pressure and declines with rising temperature. The order of adsorption quantity in CaCO₃ nanopores is H₂S > CO₂ > CH₄ based on the adsorption isotherm. At 10 MPa and 323.15 K, the interaction energies of CaCO₃ with CO₂, H₂S, and CH₄ are −2166.40 kcal/mol, −2076.93 kcal/mol, and −174.57 kcal/mol, respectively, which implies that the order of adsorption strength between the three gases and CaCO₃ is CO₂ > H₂S > CH₄. The CH₄-CaCO₃ and H₂S-CaCO₃ interaction energies are determined by van der Waals energy, whereas electrostatic energy predominates in the CO₂-CaCO₃ system. The adsorption loading of CH₄ and CO₂ are lowered by approximately 59.47% and 24.82% when the mole fraction of H₂S is 20% at 323.15 K, reflecting the weakening of CH₄ and CO₂ adsorption by H₂S due to competitive adsorption. The diffusivities of three pure gases in CaCO₃ nanopore are listed in the following order: CH₄ > H₂S ≈ CO₂. The presence of H₂S in the ternary mixtures will limit diffusion and outflow of the system and each single gas, with CH₄ being the gas most affected by H₂S. Concerning carbon storage in CaCO₃ nanopores, the CO₂/CH₄ binary mixture is suitable for burial in shallower formations (around 1000 m) to maximize the storage amount, while the CO₂/CH₄/H₂S ternary mixture sho

Surfactants and low-salinity brines have been shown to be effective for enhanced oil recovery in carbonate rocks through wettability alteration (WA). Oil wettability of carbonates is ascribed to the adsorbed organic acid components in oil. The removal of the adsorbed acids leads to WA. Previous experiments with wettability-altering surfactants have shown the following: WA is a slow process; acid removal is irreversible in most cases; surfactants can access the rock surface in water-wet regions and at three-phase contact lines rather than the entire rock surface; surfactant molecules become inactive after interactions with acids. Existing models/simulators do not incorporate the aforementioned observations. In this work, a multiphase, multicomponent, finite-difference reservoir simulator incorporating a new mechanistic model for WA was developed. The model captures the key physicochemical reactions between adsorbed acids and surfactant molecules and honors the four experimental evidences. The model was first tested at the core scale. The simulation results demonstrated that the model can accurately predict waterflood performance in rocks with various wettability. It can also effectively account for the influence of injection rates in surfactant flood experiments. The effectiveness of the surfactant, controlled by an interaction constant in the model, was found to be a dominant factor. The model was also tested for field-scale pilot tests. The results revealed that total quantity of chemicals injected and the injection rate have a more pronounced effect on oil recovery compared to the timing of surfactant treatment and the concentration of surfactant slug.

In the process of directional and horizontal well drilling, cuttings tend to settle and form a bed at the low side of the annulus due to gravity, which decreases the drilling rate and even causes accidents in severe cases. This paper analyzes the performance of a new tool, the vortex cuttings cleaner, which can be effective without rotation of the drillpipe. Based on the computational fluid dynamics (CFD) approach, together with the discrete phase, Euler, and dynamic mesh models, the vortex cuttings cleaner is investigated with respect to the turbine torque, turbine velocity, pressure drop, and cuttings transport in the annulus. The working mechanism of the vortex cuttings cleaner is clarified. Finally, field tests are conducted on the tool to evaluate its application in terms of service life, wellbore friction, and rate of penetration (ROP). The results show that the turbine can rotate continuously under hydraulic drive. The turbine torque/velocity and the tool’s pressure drop increase with increasing displacement. The cuttings transport in the annulus is jointly affected by factors such as turbine velocity, fluid velocity, and particle size. A too low or high turbine velocity is unfavorable for cuttings transport. Through the analysis of the number of particles and particle concentration, the optimal velocity is determined to be 125 rev/min. The swirling flow intensity in the annulus flow field increases with the increase in turbine velocity. Field applications suggest a service life longer than 200 hours, a notable decrease in wellbore friction, and an average increase in ROP by more than 20%. This study provides a theoretical basis for the research on wellbore cleaning tools.