Implementation of Surface Operating Conditions in Subsurface Reservoir Simulation Model by using Eclipse Simulator - A Case Study of Mari Gas Field (A Success Story), Pakistan

Abstract Field development planning of gas condensate fields using numerical simulation has many aspects to consider that may lead to a significant impact on production optimization. An important aspect is to account for the effects of network constraints and process plant operating conditions through an integrated asset model. This model should honor proper representation of the fluid within the reservoir, through the wells and up to the network and facility. Obaiyed is one of the biggest onshore gas field in Egypt, it is a highly heterogeneous gas condensate field located in the western desert of Egypt with more than 100 wells. Three initial condensate gas ratios are existing based on early PVT samples and production testing. The initial CGRs as follows;160, 115 and 42 STB/MMSCF. With continuous pressure depletion, the produced hydrocarbon composition stream changes, causing a deviation between the design parameters and the operating parameters of the equipment within the process plant, resulting in a decrease in the recovery of liquid condensate. Therefore, the facility engineers demand a dynamic update of a detailed composition stream to optimize the system and achieve greater economic value. The best way to obtain this compositional stream is by using a fully compositional integrated asset model. Utilizing a fully compositional model in Obaiyed is challenging, computationally expensive, and impractical, especially during the history match of the reservoir numerical model. In this paper, a case study for Obaiyed field is presented in which we used an alternative integrated asset modeling approach comprising a modified black-oil (MBO) that results in significant timesaving in the full-field reservoir simulation model. We then used a proper de-lumping scheme to convert the modified black oil tables into as many components as required by the surface network and process plant facility. The results of proposed approach are compared with a fully compositional approach for validity check. The results clearly identified the system bottlenecks. The model can be used to propose the best tie-in location of future wells in addition to providing first-pass flow assurance indications throughout the field's life and under different network configurations. The model enabled the facility engineers to keep the conditions of the surface facility within the optimized operating envelope throughout the field's lifetime.

Download Full-text

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

SPE Journal ◽

10.2118/191378-pa ◽

2018 ◽

Vol 23 (06) ◽

pp. 2409-2427 ◽

Cited By ~ 21

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.

Download Full-text

Post-Steamflood Reservoir Management Using a Full-Scale 3D Deterministic Thermal Reservoir Simulation Model, Wilmington Field, California

10.2118/62571-ms ◽

2000 ◽

Author(s):

J.J. Mondragon ◽

Z. Yang ◽

I. Ershaghi ◽

P.S. Hara ◽

S. Bailey ◽

...

Keyword(s):

Simulation Model ◽

Reservoir Simulation ◽

Reservoir Management ◽

Full Scale ◽

Thermal Reservoir ◽

Reservoir Simulation Model

Download Full-text

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

Journal of Energy Resources Technology ◽

10.1115/1.4041089 ◽

2018 ◽

Vol 140 (12) ◽

Cited By ~ 9

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.

Download Full-text