Methodology for Static and Dynamic Modeling of Hydrocarbon Systems Having Sharp Viscosity Gradient

2021 ◽  
Author(s):  
B.. Kayode ◽  
F.. Al-Tarrah ◽  
G.. Hursan
Keyword(s):  
Mathematical Models ◽  
Simulation Model ◽  
Dynamic Modeling ◽  
Reservoir Simulation ◽  
Fluid Viscosity ◽  
Oil Viscosity ◽  
Blind Test ◽  
Property Variation ◽  
Composition Variation ◽  
Measured Viscosity

Abstract This paper describes a methodology for delineating tar surface, incorporating it into a geological model, and the process for numerical modeling of oil viscosity variation with depth above the tar surface. The methodology integrates well log data and compositional fluid analysis to develop a mathematical model that mimics the oil's property variation with depth. While there are a good number of reservoirs that fit this description globally, there is a knowledge gap in literature regarding best practices for dealing with the peculiar challenges of such reservoirs. These challenges include; (i) how to delineate the top-of-tar across the field, (ii) modeling of Saturation Height Function (SHF) in a system where density and wettability is changing with depth, and (iii) the methodology for representing the depth-dependent oil properties (especially viscosity) in reservoir simulation. Nuclear magnetic resonance (NMR) logs were used to predict fluid viscosity using a technique discussed by Hursan et al. (2016). Viscosity regions are identified at every well that has an NMR log, and these regions are mapped from well to well across the reservoir. Within each viscosity region, the analysis results of fluid samples collected from wells are used to develop mathematical models of fluid composition variation with depth. A reliable SHF model was achieved by incorporating depth-varying oil density and depth varying wettability into the calculation of J-Function. A compositional reservoir simulation was set-up, using the viscosity regions and the mathematical models describing composition variation with depth, for the respective regions. Using information obtained from literature as a starting point, residual oil saturation was modeled as a function of oil viscosity. Original reservoir understanding places the top of non-movable oil (tar) at a constant fieldwide subsurface depth, which corresponds to the shallowest historical no-flow drillstem test (DST) depth. Mapping of the NMR viscosity regions across the field resulted in a sloping tar-oil contact (TOC), which resulted in an increase of movable hydrocarbon pore volume. The viscosity versus depth profile from the simulation model matched the observed data, and allow the simulation model better predict well performance. In addition, the simulation model results also matched the depth-variation of observed formation volume factor (FVF) and reservoir fluid density. Some wells that have measured viscosity data but no NMR logs were used as blind-test wells. The simulation model results also matched the measured viscosity at those blind-test wells. These good matches of the oil property variation with depth gave confidence, that the simulation model could be used as an efficient planning tool for ensuring that injectors are placed just-above the tar mat. The use of the simulation model for well planning could reduce the need for geosteering while drilling flank wells, leading to savings in financial costs. This paper contains a generalized approach that can be used in static and dynamic modeling of reservoirs, where oil changes from light to medium to heavy oil, underlain by tar. It contains recommendations and guidelines to construct a reliable simulation model of such systems.

Experiments and mechanistic modeling of viscosity effect on a multistage ESP performance under viscous fluid flow

2021 ◽  
pp. 095765092110149
Author(s):  
Yi Shi ◽  
Jianjun Zhu ◽  
Haoyu Wang ◽  
Haiwen Zhu ◽  
Jiecheng Zhang ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Viscous Fluid ◽  
Prediction Error ◽  
Flow Direction ◽  
Fluid Viscosity ◽  
Oil Viscosity ◽  
Viscous Fluid Flow ◽  
Viscosity Effect ◽  
High Production Rate ◽  
Pump Head

Assembled in series with multistage, Electrical Submersible Pumps (ESP) are widely used in offshore petroleum production due to the high production rate and efficiency. The hydraulic performance of ESPs is subjected to the fluid viscosity. High oil viscosity leads to the degradation of ESP boosting pressure compared to the catalog curves under water flow. In this paper, the influence of fluid viscosity on the performance of a 14-stage radial-type ESP under varying operational conditions, e.g. rotational speeds 1800–3500 r/min, viscosities 25–520 cP, was investigated. Numerical simulations were conducted on the same ESP model using a commercial Computational Fluid Dynamics (CFD) software. The simulated average pump head is comparable to the corresponding experimental data under different viscosities and rotational speeds with less than ±20% prediction error. A mechanistic model accounting for the viscosity effect on ESP boosting pressure is proposed based on the Euler head in a centrifugal pump. A conceptual best-match flowrate QBM is introduced, at which the impeller outlet flow direction matches the designed flow direction. The recirculation losses caused by the mismatch of velocity triangles and other head losses resulted from the flow direction change, friction loss and leakage flow etc., are included in the model. The comparison of model predicted pump head versus experimental measurements under viscous fluid flow conditions demonstrates good agreement. The overall prediction error is less than ±10%.

Robust Life-Cycle Production Optimization With a Support-Vector-Regression Proxy

SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2409-2427 ◽  
Author(s):  
Zhenyu Guo ◽  
Albert C. Reynolds
Keyword(s):  
Life Cycle ◽  
Simulation Model ◽  
Support Vector Regression ◽  
Reservoir Simulation ◽  
Production Optimization ◽  
Forward Model ◽  
Support Vector ◽  
Well Control ◽  
Steepest Ascent ◽  
Reservoir Simulation Model

Summary We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.

A Proxy Model for Predicting SAGD Production From Reservoirs Containing Shale Barriers

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Jingwen Zheng ◽  
Juliana Y. Leung ◽  
Ronald P. Sawatzky ◽  
Jose M. Alvarez
Keyword(s):  
Simulation Model ◽  
Network Model ◽  
Reservoir Simulation ◽  
Oil Sands ◽  
Training Set ◽  
Steam Assisted Gravity Drainage ◽  
Proxy Model ◽  
Reservoir Simulation Model ◽  
The Individual ◽  
Operational Constraints

Artificial intelligence (AI) tools are used to explore the influence of shale barriers on steam-assisted gravity drainage (SAGD) production. The data are derived from synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints gathered from the Suncor's Firebag project, which is representative of Athabasca oil sands reservoirs. The underlying reservoir simulation model is homogeneous and two-dimensional. Reservoir heterogeneities are modeled by superimposing sets of idealized shale barrier configurations on this homogeneous reservoir model. The individual shale barriers are categorized by their location relative to the SAGD well pair and by their geometry. SAGD production for a training set of shale barrier configurations was simulated. A network model based on AI tools was constructed to match the output of the reservoir simulation for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for arbitrary configurations of shale barriers. The predicted results were consistent with the results of the SAGD simulation model with the same shale barrier configurations. The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parametrization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from SAGD field production data.

Three-Dimensional Dynamic Modeling of the Human Knee to Include Tibio-Femoral and Patello-Femoral Joints

2001 ◽  
Author(s):  
Dumitru Caruntu ◽  
Mohamed Samir Hefzy
Keyword(s):  
Knee Joint ◽  
Mathematical Models ◽  
Dynamic Modeling ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Human Knee ◽  
Two Dimensional Model ◽  
Rigid Contact ◽  
Locomotor Activities

Abstract Most of the anatomical mathematical models that have been developed to study the human knee are either for the tibio-femoral joint (TFJ) or patello-femoral joint (PFJ). Also, most of these models are static or quasistatic, and therefore do not predict the effects of dynamic inertial loads, which occur in many locomotor activities. The only dynamic anatomical model that includes both joints is a two-dimensional model by Tumer and Engin [1]. The model by Abdel-Rahman and Hefzy [2] is the only three dimensional dynamic model for the knee joint available in the literature; yet, it includes only the TFJ and allows only for rigid contact.

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