Flow Simulator Research Articles

In-vitro validation of coronary physiology assessment with 5 French guiding catheters

BackgroundThe trans-radial approach for cardiac catheterization led to an increasing adoption of 5 French (F) catheters. We aim to evaluate reliability and reproducibility of coronary physiology assessment performed with 5F guiding catheter (GC). MethodsPhysiological measurements were performed in a coronary flow simulator, which provides two pulsatile flows, the baseline and hyperaemic flows. Two screws, positioned proximally and distally to the distal sensor of a pressure-temperature guidewire, were used to determine various combinations of stenoses and distal obstructions, simulating different pathophysiological conditions. For each setting, 5 measurements of fractional flow reserve (FFR), coronary flow reserve (CFR) and index of microvascular resistance (IMR) were performed with 6F and 5F GCs. ResultsA total amount of 190 measurements were performed, 95 with 6F GC and 95 with 5F GC. Minimal differences between 6F and 5F GCs were detected for FFR [0.91 (IQR: 0.87–0.94) and 0.87 (IQR: 0.82–0.92) respectively, p < 0.001] and IMR (16.5 ± 8.8 and 15.4 ± 8.3 respectively, p = 0.001). Mean CFR was comparable between 6F and 5F GCs (3.6 ± 1.1 and 3.5 ± 0.7 respectively, p = 0.38). Misclassification rates were 1.0 %, 1.0 % and 0 % for FFR, CFR and IMR, respectively. According to Passing-Bablok analysis, an excellent agreement between 6F and 5F GCs was demonstrated for FFR and IMR, and a modest agreement for CFR. All measurements with 5F GC showed high reproducibility. ConclusionsIn our in-vitro model, a complete physiological assessment including FFR, CFR and IMR resulted substantially comparable between 6F and 5F GCs. Further in-vivo analysis is required to support these findings.

A wet muck entry model: a case study for long-term planning at El Teniente mine

ABSTRACT Wet muck is a problem in underground mines due to the consequences it brings to the safety of workers, equipment, mining infrastructure, drawpoints, production drifts, and productive sectors, which can generate a loss in reserves. This paper describes a mathematical model to estimate wet muck entry for long-term planning applications at El Teniente. Three basins of El Teniente were included in the study of wet muck control: North, Centre, and South. Each basin includes mines with different characteristics in each exploitation sector. Consequently, models were built for each of the basins to represent its distinct reality. The models have been embedded in machine-learning software that estimates hazards associated with the extraction process for underground mines. To create the models, several variables – each associated with historical extraction – were investigated, including the extraction ratio and the height of draw, amount of water entering the cave, season of the year, presence of mud in neighbouring drawpoints, sectors closed due to wet muck above, and changes in surface or depressions. This study also includes granular flow variables and lithologies. In addition, fragmentation was included and estimated with a granular flow simulator, and the information was validated and calibrated with data from the El Teniente mine. Results indicate that our classification models can reproduce the wet muck phenomenon with an acceptable precision between 69% and 75% and an average tonnage error per drawpoint of 6%−15%. The approach detailed here to create models could be applied to other mine sites if the necessary data are available.

The main features of compositional modeling algorithms of modern reservoir flow simulators

Modern industrial reservoir flow simulators are integrated software products consisting of several packages. Modeling of gas injection with various compositions, or hydrocarbon recovery from gas-condensate and oil-gas-condensate reservoirs, or thermal recovery methods requires a detailed account for composition of the fluid system. In these cases, compositional (multi-component) flow simulation is used to calculate component distribution between the coexisting phases, the number and the fractions of the phases, together with the flow equations for fluid components.. For this purpose, the flow simulator solves the thermodynamic subproblem using basic methods and algorithms analogous to the fluid modeling (PVT modeling) packages. The paper describes the main principles and specific features of implementation of the compositional modeling algorithms in several modern reservoir flow simulators. Keywords: compositional modeling; multicomponent flow model; reservoir flow simulator; isothermal and non-isothermal compositional modeling.

Formation and Hazard Analysis of Landslide Damming Based on Multi-Source Remote Sensing Data

Remote sensing plays an increasingly important role in the investigation of natural hazards, not only by obtaining specific data related to hazards, but also by realizing targeted research by combining with other data and/or technologies. Small-scale landslide hazard chain events occur frequently in mountainous areas with fragile geological environments and have strong destructive effects, yet have been somewhat understudied. This paper analyzes the Zhoujiaba (ZJB) landslide hazard chain that occurred in Longnan City on 18 August 2020. On the basis of the comprehensive application of multi-source remote sensing data, combined with time-series InSAR technology, electrical resistivity tomography (ERT), and numerical simulations, we studied the formation mechanism, damming characteristics, and potential outburst scenarios of this event. Our research suggests that geological structure and strong natural weathering are the preconditions for landslide development, which is eventually induced by extreme rainfall. Specific topographic conditions determine the rapid sliding and accumulation of landslide materials, and ultimately result in river damming. Our simulation results showed that a flood, rather than a debris flow, will be the result of dam outburst. When the simulated upstream inflow is 1.5 times that when the landslide occurred, 68% of the downstream village area will be flooded. The artificial spillway can effectively reduce the scale of the potential outburst flood, but there remains a risk of dam failure owing to the shallow depth. Our study of the hazard chain of a small-scale landslide using a combination of methods will provide a valuable reference for the analysis and treatment of similar hazard chains.

Pores collapse hysteresis damage evaluation in depletion-dependent oil reservoirs using an asymptotic-integro-differential perturbation method

This work proposes a new asymptotic-perturbative solution for transient pores collapse hysteresis modeling in depletion-dependent oil reservoirs with compaction effects during alternating loading/unloading cycles. The nonlinear hydraulic diffusivity equation (NHDE) is perturbed through a first-order expansion technique using the depletion-dependent permeability, k(p) as a perturbation parameter, ϵ. A new hysteretic deviation factor is presented for flow (drawdown) and well-shut (buildup) periods. This factor represents the permeability deviation during the drawdown and its partial restoration in the buildup period. The model calibration is performed by a porous media numerical oil flow simulator named IMEX®, and the results presented high accuracy for the drawdown and buildup of hysteretic and non-hysteretic cases (when the pores collapse did not occur yet). The practical uses of the model developed in this work are related to identifying flow regimes and hysteresis responses in pressure-sensitive reservoirs, estimating buildup pressure, and oil flow rates specification to prevent severe hysteretic behavior and history matching during reservoir surveillance. Two case studies related to different reservoir layers are researched, and for both cases, the results from the log–log plots analysis have shown that the infinite-acting-radial oil flow (1/2 value of the slope of the pseudo-pressure derivative) was identified. In addition, the log–log analysis also shows that the shut-in pressure has an influence on permeability loss. The main advantages of the solution derived in this work are the simple implementation, practical graphical analysis of the pores collapse hysteresis effect, the possibility of simulating different boundary conditions and well-reservoir settings, and the requirements of only a few pressure and permeability field data to input in the deviation factor. The solution proposed can be applied to choose the production time to shut the well and monitor the adequate oil flow rate during the production curve.

Patient-specific commissural alignment for ACURATE neo2 implantation in degenerated surgical bioprostheses.

Valve-in-valve TAVI to treat failing surgical aortic valves (SAVs) is increasingly performed, and commissural alignment is a key technical aspect in such procedures. Surgeons optimize valve alignment, accounting for potential coronary eccentricity and achieving a patient-specific optimized commissural orientation, representing the ideal target for TAVI alignment. Therefore, here we present a dedicated stepwise valve-in-valve implantation technique using the ACURATE neo2. In a specific SAV postoverlap view, isolating one surgical post to the right of the screen representing the target for alignment, rotational orientation of the TAVI commissures, matching the SAV orientation, is achieved and verified before implantation. This technique has been tested in a patient-specific three-dimensionally-printed aortic root anatomy, attached to a pulsatile flow simulator, allowing for native-like simulation of coronary cannulations under fluoroscopy, and enabling detailed assessment with fluoroscopic as well as direct videographic visualization. Furthermore, the technique is exemplified by providing an educational clinical case example.

Three‐axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator

AbstractThe field of geophysics faces the daunting task of monitoring complex reservoir dynamics and imaging carbon dioxide storage up to several decades into the future. This presents numerous challenges, including sensitivity to parameter changes, resolution of obtained results and the cost of long‐term deployment. To effectively store CO2 subsurface, it is necessary to monitor and account for the injected CO2. The gravity method provides several advantages for CO2 monitoring, as changes in fluid saturation correspond directly and uniquely to observed density changes. Three‐axis borehole gravity has demonstrated significant promise as a next‐generation tool for reliably monitoring reservoir dynamics across a range of depths and sizes. However, the gravity inverse problem is highly ill‐posed, necessitating regularization that incorporates prior knowledge. To address this issue, we propose using a feed‐forward neural network, a machine learning method, to invert time‐lapse three‐axis borehole gravity data and monitor CO2 movement within a reservoir. By training the neural network on models that analyse changes in density and corresponding gravity responses resulting from perturbations made to the reservoir model, we can create scenarios that train the algorithm to identify unexpected CO2 migration in addition to the normal movement of CO2. Our method is demonstrated using reservoir models for the Johansen formation in offshore Norway. We convert reservoir saturation models into density changes and generate their corresponding three‐axis gravity data in a set of boreholes. Our results show that the developed machine learning inversion algorithm has high reliability and resolution for imaging density change associated with CO2 plumes, as demonstrated in the Johansen reservoir models utilized by the simulator. We also investigate machine learning inversion using regularization parameters and show that it is robust, with a strong tolerance for higher levels of noise. Our study demonstrates that the developed machine learning algorithm is a powerful tool for inverting three‐axis borehole gravity data and monitoring the migration and long‐term storage of injected CO2.

A Coupled Poro-Elastic Fluid Flow Simulator for Naturally Fractured Reservoirs

Naturally fractured reservoirs are characterized by their complex nature due to the existence of natural fractures and fissures within the rock formations. These fractures can significantly impact the flow of fluids within the reservoir, making it difficult to predict and manage production. Therefore, efficient production from such reservoirs requires a deep understanding of the flow behavior via the integration of various geological, geophysical, and engineering data. Additionally, advanced simulation models can be used to predict reservoir behavior under different production scenarios and aid in decision making and effective management. Accordingly, this study presents a robust mathematical two-phase fluid flow model (FRACSIM) for the simulation of the flow behavior of naturally fractured reservoirs in a 3D space. The mathematical model is based on the finite element technique and implemented using the FORTRAN language within a poro-elastic framework. Fractures are represented by triangle elements, while tetrahedral elements represent the matrix. To optimize computational time, short to medium-length fractures adopt the permeability tensor approach, while large fractures are discretized explicitly. The governing equations for poro-elasticity are discretized in both space and time using a standard Galerkin-based finite element approach. The stability of the saturation equation solution is ensured via the application of the Galerkin discretization method. The 3D fracture model has been verified against Eclipse 100, a commercial software, via a well-test case study of a fractured basement reservoir to ensure its effectiveness. Additionally, the FRACSIM software successfully simulated a laboratory glass bead drainage test for two intersected fractures and accurately captured the flow pattern and cumulative production results. Furthermore, a sensitivity study of water injection using an inverted five-spot technique was tested on FRACSIM to assess the productivity of drilled wells in complex fractured reservoirs. The results indicate that FRACSIM can accurately predict flow behavior and subsequently be utilized to evaluate production performance in naturally fractured reservoirs.

Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico

This article aims to obtain and evaluate medium-term operating policies for the hydrothermal scheduling problem by using the stochastic dual dynamic programming (SDDP) approach. To this end, to feed the mathematical model and build the probability distribution functions that best fit each month of the actual inflow volume, monthly inflow data recorded from 1938 to 2018 for the Infiernillo reservoir located in Mexico were employed. Moreover, we simulated inflow volume scenarios using the Monte Carlo method for each month of a one-year planning period. The SDDP approach to solving the optimization problem consisted of the simulation of one forward scenario per iteration and the stabilization of the total operating cost as a convergence criterion, which results in an operating policy. We then assessed its quality by estimating the one-sided optimality gap. It is worth mentioning that the best operation policy required scenario trees of up to 17,000 inflow realizations per stage. Additionally, to study the evolution of the expected value along the planning horizon of the main variables involved in the medium-term hydrothermal scheduling problem, we simulated the best operation policy over 10,000 inflow scenarios. Finally, to show the practical value of the proposed approach, we report its computational complexity.

A Lagrangian method for slurry flow modeling in hydraulic fractures

We developed a new numerical simulator of slurry flow in hydraulic fractures. It is based on a continuum model developed under the assumptions of lubrication theory. We consider slurry containing fluid and proppant components. Proppant particles settle under gravity and bridge in the flow domain. Fluid fluxes are calculated from an elliptic boundary value problem for given fracture geometry and sources/sinks distribution. We solve it using a conservative finite volume method and then apply empirical closure relations to get the velocity field for solids. The transport of slurry components is described by nonlinear hyperbolic equations with fluxes depending on proppant concentration and particle size/fracture width ratio. These equations admit solutions with concentration shock waves and rarefaction fans. They must be represented accurately in the computational algorithm. We utilize an Eulerian approach for the discretization of nonlinear proppant fluxes. After getting the fluxes on the interfaces between Eulerian grid cells we follow a Lagrangian approach to simulate the proppant transport along the given velocity field. In this study we discuss key implementation aspects: time step selection, regularization, fluxes discretization, seeding, reseeding, and movement of Lagrangian particles. We verify the simulator on benchmark solutions focused on advection, batch proppant settling, slurry slumping and fluid displacement by proppant.

Surfactant-enhanced in-situ oxidation of DNAPL source zone: Experiments and numerical modeling

In this study we investigate the synergetic effects of combining surfactant-enhanced dissolution with in-situ oxidation of a pool-dominated PCE DNAPL source zone entrapped in porous media. Flow cell flushing experiments packed with silica sand and natural calcareous soil were conducted with a surfactant (Tween 80) and permanganate (MnO4−) used as dissolution and oxidation agents, respectively. The resultant breakthrough curves exhibited a multiple step behavior with mass removal controlled in the latter stages by the less-accessible DNAPL mass. DNAPL spatial architecture, flow-field heterogeneity, and flushing solution all influenced the remediation effort. When taking into account both the surfactant-enhanced dissolution and permanganate oxidation processes, mass-flux reduction/mass-removal behavior relationships indicated that the inclusion of oxidation in the remediation scheme delayed the drop in mass flux from the source zone, leading to improved DNAPL removal efficiency. Numerical modeling was also performed to further evaluate the efficacy of the surfactant-enhanced chemical oxidation of DNAPL PCE with permanganate. The system of reaction equations available in the multiphase flow simulator UTCHEM were adapted to simulate the chemical oxidation process in the presence of a surfactant. The model results yield lower oxidation reaction rate constants in the presence of Tween 80, indicating that Tween 80 can interfere with the reaction rate. However, the increase in the solubility of PCE in the presence of Tween 80 more than compensates for the decrease in reaction rate constant. Overall, for Tween 80/MnO4− applied at sufficient dosages, more efficient DNAPL zone remediation was achieved compared to surfactant flushing or permanganate oxidation alone.

Experimental study of liquid/dense-gas pipe flow

The pre-salt reservoirs play a significant role in the current Brazilian ultradeep water offshore scenario. Their importance is expected to increase soon. The fluids are transported through horizontal, inclined, and vertical wells of several kilometers, and due to the environmental conditions at the reservoir, dense-gas/liquid flow occurs in the pipelines. However, the literature lacks experimental data for gas-liquid flow with densities of the same order of magnitude, which affects the mechanistic model's accuracy for flow patterns, pressure gradients, and holdup predictions. To improve the understanding of that specific two-phase flow, an experimental apparatus was built, and an experimental study was carried out using sulfur hexafluoride (100 kg/m3) and mineral oil (850 kg/m3) at medium pressures. A density ratio lower than 10 was obtained for steady-state horizontal flow in a 50.8-mm-i.d. stainless-steel pipe, and flow patterns, pressure gradient, and holdup were measured. The experimental results show the effect of the low-density ratio on the stratified to non-stratified flow-pattern transition and the occurrence of the dual-continuous flow pattern at high mixture superficial velocity. Moreover, the pressure gradient and holdup data were compared with the results of a commercial multiphase flow simulator, showing good agreement.

Probabilistic volcanic mass flow hazard assessment using statistical surrogates of deterministic simulations

Probabilistic volcanic hazard assessments require (1) an identification of the hazardous volcanic source; (2) estimation of the magnitude-frequency relationship for the volcanic process; (3) quantification of the dependence of hazard on magnitude and external conditions; and (4) estimation of hazard exceedance from the magnitude-frequency and hazard intensity relationship. For volcanic mass flows, quantification of the hazard is typically undertaken through the use of computationally expensive mass flow simulators. However, this computational expense restricts the number of samples that can be used to produce a probabilistic assessment and limits the ability to rapidly update hazard assessments in response to changing source probabilities. We develop an alternate approach to defining hazard intensity through a surrogate model that provides a continuous estimate of simulation outputs at negligible computational expense, demonstrated through a probabilistic hazard assessment of dome collapse (block-and-ash) flows at Taranaki volcano, New Zealand. A Gaussian Process emulator trained on a database of simulations is used as the surrogate model of hazard intensity across the input space of possible dome collapse volumes and configurations, which is then sampled using a volume-frequency relationship of dome collapse flows. The demonstrated technique is a tractable solution to the problem of probabilistic volcanic hazard assessment, with the surrogates providing a good approximation of the simulator, and is generally applicable to volcanic hazard and geo-hazard assessments that are limited by the demands of numerical simulations and changing source probabilities.

Thermo-hydro-mechanical-chemical coupling in chalk reservoirs: Impact on fluid flow and deformation during water injection

Reliable prediction of the rate- and fluid-dependent evolution of elasto-plastic strain in chalk is essential to minimize the risk associated with sea-floor subsidence and subsurface deformation. Experimental observations show that physicochemical interaction between the aqueous solutions and rock surface causes the change of mechanical properties in chalk. Here, we quantify the experimental observations of hydrostatic pore collapse strength and bulk modulus of water-saturated chalk specimens as a function of temperature and sulfate concentration. Further, a wrapper is developed to couple the non-isothermal transport of the multi-phase flow simulator with the geomechanics simulator to consider the impact of the coupled interactions on fluid transport and reservoir deformation. In this study, the interplay between thermo-hydro-mechanical-chemical processes in chalk reservoirs is investigated by means of numerical experiments. Computational results suggest that reservoir pressure depletion, temperature changes, and water weakening play significant roles in controlling the reservoir's deformation and production. Our study also confirms that accounting explicitly for the coupled THMC processes in reservoir simulations of water flooding in chalk is required for reliable prediction of reservoir responses.

Development of a numerical simulator for modeling seismic EOR

Natural earthquakes, culture noise, and artificially induced mechanical waves have been reported to affect the productivity of oil wells. Sources emitting waves in the seismic frequency range, between 1 and 1000 Hz, have been used as an enhanced oil recovery (EOR) tool for improving oil production rates. Theories suggest several mechanisms for the increased oil production rate including (AbuZaid et al., 2021): overcoming the capillary forces holding the oil phase in place (Alzahrani and Abbas, 2020), improving the relative permeability of oil, and (Ariadji, 2005) decreasing viscosity as temperature rises from waves attenuation. In this work, a fully implicit modified black oil simulator has been developed. The fluid flow simulator is coupled with a ray tracing module for simulating waves propagation in the model. Wave simulation is used to calculate the effect of seismic waves on capillary pressure, oil relative permeability, and reservoir temperature. It is also used to investigate the applicability of the proposed mechanisms for explaining the observed enhancement in oil recovery due to seismic waves. This seismic stimulation simulator can provide insights on the response of wells’ production rates at various field characteristics and seismic tool(s) operation conditions, explain field results, and design field implementation of seismic EOR.

Dynamic texture analysis using Temporal Gray scale Pattern Image for water surface velocity measurement

Water surface velocity (WSV) is one of the critical parameters in hydrology. The development of non-intrusive measurement of this parameter using cameras is increasing. Traditionally, measuring WSV using a camera utilizes tracking of moving objects on the water surfaces. Recently, another method has emerged that utilizes the movement of water ripples to estimate the WSV. This paper proposes a novel method for estimating WSV based on camera measurements. The authors call this method Temporal Gray-scale Pattern Image (TGPI) since it extracts Gray-scale patterns of pixels of water flow video in the temporal domain using the XOR operator and create new image. The new image pattern formed is then used to predict WSV using predictor. There are four predictors being compared, namely Multiple Input Linear Regression (MILR), Multiple Input Logistic Regression (MILgR), Convolutional Neural Network Regression (CNN-R), and Convolutional Neural Network Classification (CNN-K). CNN-R and CNN-K predict WSV directly from the results of TGPI. Meanwhile, MILR and MILgR predict WSV from five TGPI features. The five features are the Mean and Median of the Histogram, Mean and Median of the Histogram of Oriented Gradient (HOG), and the Maximum Mid-Value of the Fast Fourier Transform (FFT). MILR and CNN-R are predictors for regression problems, so the testing metrics for them are Trend-Line equation and R2 from the predicted WSV and actual values graph. Meanwhile, for MILgR and CNN-K, which are predictors for classification problems, the testing metrics are Confusion Matrix (CM), Accuracy, Precision, Recall, and F1-Score. To test the four methods without distinguishing their predictor types, a 2-dimensional histogram graph is used. The data-set used for training and testing is video footage of water flow with known WSV. The WSV measurement points used in this study are 1.7 m/s, 3.1 m/s, and 4.2 m/s. The video dataset and these three points are generated by the Mini Open Channel Water Flow Simulator (MOCWFS) developed by the author in this study. From the comparison results, it can be seen that the classification type predictor is superior to the regression type. For the regression type predictor, MILR is better than CNN-R. Meanwhile, for the classification type, CNN-K is superior to MILgR. The best accuracy produced by CNN-K is 98.4%. Although there are shortcomings, the TGPI method is quite feasible for predict Water Surface Velocity.

Current and emerging deep-learning methods for the simulation of fluid dynamics

Over the last decade, deep learning (DL), a branch of machine learning, has experienced rapid progress. Powerful tools for tasks that have been traditionally complex to automate have been developed, such as image synthesis and natural language processing. In the context of simulating fluid dynamics, this has led to a series of novel DL methods for replacing or augmenting conventional numerical solvers. We broadly classify these methods into physics- and data-driven methods. Physics-driven methods, generally, tune a DL model to provide an analytical and differentiable solution to a given fluid dynamics problem by minimizing the residuals of the governing partial differential equations. Data-driven methods provide a fast and approximate solution to any fluid dynamics problem that shares some physical properties with the observations used when tuning the DL model’s parameters. Meanwhile, the symbiosis of numerical solvers and DL has led to promising results in turbulence modelling and accelerating iterative solvers. However, these methods present some challenges. Exclusively data-driven flow simulators often suffer from poor extrapolation, error accumulation in time-dependent simulations, as well as difficulties in training against turbulent flows. Substantial effort is, therefore, being invested into approaches that may improve the current state of the art.

It’s Possible That Salt Clogging Is Lowering Your Gas Production

Salt buildup around producing wells is known for causing drastic drops in gas production. “Salt precipitation in gas wells (can) cause clogging, and severe production declines that may happen in days,” said Cíntia Gonçalves Machado, a scientist at TNO, which supports operators working in the waters off the Netherlands where salt blockages are a significant issue for producers. She recently presented a paper at the SPE Reservoir Simulation Conference on a new application that cuts the time and cost required to analyze the impact of salt on production and evaluate treatment plans (SPE 212257). The goal for TNO and its partner, Equinor, was to create an open-source simulator built on to a familiar engineering tool allowing “engineers to study how salt precipitation may affect production with a tool that is relatively cheap computationally, and also easy to use and visualize,” Machado wrote in an email. Specifically, the team that developed it started with a black-oil model in the open-source OPM Flow simulator and added elements that simulate salt precipitation, which happens as rapidly evaporating gas pulls water molecules out of salty brine, and estimate the effect on permeability. Other programs can do all that. What makes this one different is that it can run on an engineer’s laptop and can run a simulation in minutes to hours, while previous models require high-powered computer systems running for hours to days, Machado said. The early users are likely to be the small cadre of experts working on the extreme cases where salt buildup happens so rapidly that regular injections of fresh water are needed to dissolve the blockages. But Equinor also wanted a tool to help it identify wells where salt blockages are having a slower-building impact that is not obvious. “They see wells with a reduction in gas production and want to have some tool to understand and predict it,” said Paul Egberts, a staff mathematician at TNO who applied his years of salt-modeling experience to this project. The team also included Odd Steve Hustad, a reservoir technology specialist at Equinor who advised on how to accurately incorporate the physical properties of the brine into the black‑oil model. The paper described their work: “The black-oil transport equations are extended to allow the gas to contain vaporized water in addition to vaporized oil. The salt-transport equation is modified to account for a solid salt precipitate. The permeability reduction is dealt with through a mobility multiplier depending on the porosity change.” Salt precipitation is a possible source of trouble in wells where the pressure drops sharply to a low level near the wellbore. That triggers rapid gas evaporation, which pulls water droplets out of the brine. In wells with high water saturation and salinity, if the evaporation rate is high enough, it can reduce the liquid to below the level needed to dissolve salt, leading to the formation of salt crystals that clog the flow paths for gas. Egberts added that the actual production impact will depend on the vagaries of the reservoir.

The modeling of fault activation, slip, and induced seismicity for geological [formula omitted] storage at a pilot-scale site in the Janggi Basin, South Korea

We investigate the effects of possible fault activation and slip during geological CO2 storage operations numerically. To this end we develop a numerical simulator by coupling a multiphase flow simulator with an existing geomechanics simulator in order to model fault slip and seismic events induced by reservoir pressurization. After validation and verification of the developed coupled simulator, we apply it to a pilot-scale site of geological CO2 storage located in the Janggi Basin, South Korea. We design a fault structure with a fault core and surrounded damage zones, assuming the fault to be activated along the fault core only. Zero-thickness interface elements are employed in the numerical model to represent the fault core. We perform numerical tests with different injection scenarios to evaluate the impact of pore pressure on fault activation and its post-failure behavior. We model occurrence and the magnitude of seismic events, identifying sudden changes of the moment magnitude at the onset of fault activation. Specifically, we take CO2 injection with 100 , 200, 400 ton/day for 20 years, from which the cumulative moment magnitudes (Mw’s) of 1.46, 2.36 and 3.11 are estimated, respectively. However, the maximum instantaneous changes of Mw occur at the onset of fault activation for all the injection cases (i.e., approximately 0.1, 0.36, 0.75, respectively). As the injection rate increases, fault activation occurs early and the magnitude of induced seismicity becomes large, but substantial reduction of the injection rate can weaken following induced seismicity. Interestingly, the location of maximum surface lift does not correspond to the injection well due to heterogeneity of geology and geomechanics. We thus expect relatively low risk of induced seismicity overall within the range of injection rates considered in this study, but monitoring surface uplift and seismic signals is still necessary to prevent any unexpected events due to uncertainty of parameters.

Poroelastic-Flow Model for Permeability Loss Management in Biot’s Stress-Sensitive Oil Reservoirs with Finite Extent Hydraulic Fractures During Well-Reservoir Drawdown

Summary The evaluation of geomechanical effects and fluid flow related to pressure transient phenomena in fractured Biot’s stress-sensitive oil reservoirs is essential to minimize the mechanical formation damage and extend the well-reservoir life cycle for economical production. Therefore, the management of the damage caused by effective permeability loss in this type of reservoir becomes essential to productivity maintenance. This paper proposes a new unsteady-state poroelastic solution for the nonlinear hydraulic diffusivity equation (NHDE) in Biot’s effective stress-sensitive reservoirs fully penetrated by fractured oil wells. The hydraulic fracture in the proposed mathematical modeling is finite with tip effects and crosses the whole reservoir net-pay. A new permeability stress-sensitive pseudopressure m⁢(σ′) is developed, and the solution of the NHDE is derived in terms of this function. The NHDE is expanded in a first-order asymptotic series, and a poroelastic integro-differential solution coupled to a Green’s function is used to represent the source/sink term. A set of pore pressure and permeability data is used from geomechanical literature and transformed into effective stress through Biot’s equation. The effects of the Biot’s coefficient, overburden stress, oil flow rate, fracture’s tip, and proppant porosity arrangements are simulated. The results show that these parameters are essential to minimize formation damage. Model calibration is performed using a numerical oil flow simulator named IMEX®, widely used in the oil industry. The accuracy, ease of implementation, and low computational costs constitute the main advantages of the model addressed in this paper. Hence, it may be a valuable and attractive mathematical tool to identify flow regimes, providing permeability loss control and supporting well-reservoir management.