Numerical procedure to effectively assess sequestration capacity of geological structures

Nafta-Gaz ◽  
2021 ◽  
Vol 77 (12) ◽  
pp. 783-794
Author(s):  
Wiesław Szott ◽  
◽  
Krzysztof Miłek ◽  
Keyword(s):  
Simulation Model ◽  
Numerical Procedure ◽  
Geological Structure ◽  
Optimization Scheme ◽  
Geological Structures ◽  
Injection Wells ◽  
Relaxation Phase ◽  
Model Initialization ◽  
Reservoir Simulation Model ◽  
Induced Fractures

The paper presents a numerical procedure of estimating the sequestration capacity of an underground geological structure as a potential sequestration site. The procedure adopts a reservoir simulation model of the structure and performs multiple simulation runs of the sequestration process on the model according to a pre-defined optimization scheme. It aims at finding the optimum injection schedule for existing and/or planned injection wells. Constraints to be met for identifying the sequestration capacity of the structure include a no-leakage operation for an elongated period of the sequestration performance that contains a relaxation phase in addition to the injection one. The leakage risk analysis includes three basic leakage pathways: leakage to the overburden of a storage formation, leakage beyond the structural trap boundary, leakage via induced fractures. The procedure is implemented as a dedicated script of the broadly used Petrel package and tested on an example of a synthetic geologic structure. The script performs all the tasks of the procedure flowchart including: input data definitions, simulation model initialization, iteration loops control, simulation launching, simulation results processing and analysis. Results of the procedure are discussed in detail with focus put on various leakage mechanisms and their handling in the adopted scheme. The overall results of the proposed procedure seem to confirm its usefulness and effectiveness as a numerical tool to facilitate estimation of the sequestration capacity of an underground geological structure. In addition, by studying details of the procedure runs and its intermediate results, it may be also very useful for the estimation of various leakage risks.

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.

scholarly journals Geological Structure Analysis to Determine the Direction of the Main Stress at Western Part of Kolok Mudik, Barangin District, Sawahlunto, West Sumatera

2017 ◽  
Vol 2 (1) ◽  
pp. 46
Author(s):  
Miftahul Jannah ◽  
Adi Suryadi ◽  
Muchtar Zafir ◽  
Randi Saputra ◽  
Ihsanul Hakim ◽  
...  
Keyword(s):  
Structure Analysis ◽  
Normal Fault ◽  
Geological Structure ◽  
Reverse Fault ◽  
Geological Structures ◽  
The North ◽  
Main Stress

On the study area there are three types of structure, those are fault, fold and joint. Types of fault were found  in the study area, reverse fault with the strike/dip is N215oE/75o, normal fault has a fault directions N22oE and N200oE with pitch 35o, and dextral fault with pitch 10o and strike N219oE. Fold and joint structures used to determine the direction of the main stress on the study area. Further, an analysis used stereonet for data folds and joints. So that from the data got three directions of main stress, those are Northeast – Southwest (T1), North – South (T2) and Southeast – Northwest (T3). On the Northeast – Southwest (T1) stress there are four geological structures, anticline fold at ST.3 , syncline folds at ST. 13a, ST. 13b, ST. 13c and ST. 33, chevron fold at ST. 44 and joint at ST. 2. On the North – South (T2) stress there are three geological structures, those are syncline fold at ST. 35, anticline fold at ST. 54 and joints at ST. 41, ST. 46 and ST. 47. On the Southeast – Northwest (T3) stress were also three geological structures, those are chevron fold at ST 42a, overturned fold at ST. 42b, syncline fold at ST. 42c and joints at ST. 5 and ST. 34.

scholarly journals The theory and application of the random dislocation directional seismic wave technique in a tunnel environment

2019 ◽  
Vol 16 (5) ◽  
pp. 939-949
Author(s):  
Yonggao Yue ◽  
Tao Jiang ◽  
Jingye Wang ◽  
Yunfeng Chao ◽  
Qi Zhou ◽  
...  
Keyword(s):  
Seismic Wave ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Geological Structure ◽  
Detection Accuracy ◽  
Detection Distance ◽  
Geological Structures ◽  
Multiple Wave ◽  
Geological Conditions ◽  
Wave Technique

Abstract Performing exact predictions of geological conditions for tunnel construction is important for ensuring safe and quick tunnel engineering. Weak effective signals and strong random noise are the main factors that affect the distance and precision of tunnel seismic detection. Considering that directional seismic wave (DSW) technology has the ability to enhance target signals and suppress random noise, we attempt to apply this method to solve the problems of low detection accuracy and short detection distance. However, the process of data processing with the DSW technique generates false multiple wave interference (FMWI), which can lead to the misinterpretation of geological structures. This study analyses the origins of FMWI and presents the random dislocation directional seismic wave (RDDSW) method to suppress this interference. The results of a numerical simulation indicate that the FMWI is effectively suppressed and that the signal-to-noise ratio of the data is increased by approximately N times through use of the N-element RDDSW technique. In the ideal case, only spherical diffusion attenuation is considered, and the detection distance increases by approximately $\scriptstyle\sqrt N $ times. In addition, this method is also effective for signals from curved events, thereby improving the precision of the analysis of the geological structure of the tunnel. Furthermore, the field data results further verify that the RDDSW technique can significantly suppress interference and thus improve the quality of the data at little cost. Hence, the RDDSW technique has great significance for accurately predicting the geological structures of tunnels and increasing the detection distance in tunnels.

Application of 3D Geomechanics Modeling to Enhance Reservoir Simulation Model Forecast in Terrigenous Fractured Reservoir (Russian)

2020 ◽  
Author(s):  
Daniil Valeryevich Balin ◽  
Ilya Georgievich Alekhin ◽  
Vyacheslav Igorevich Brovko ◽  
Anton Georgievich Naimyshin
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
Fractured Reservoir ◽  
Model Forecast ◽  
Reservoir Simulation Model
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