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Parallel Processing of Invasive Seepage for Large-Scale High-Resolution Simulation of Secondary Oil and Gas Migration

A parallel processing and migration technology, applied in special data processing applications, geographic modeling, geophysical measurement, etc., to achieve the effect of enhancing computing performance and scalability, and shortening the running time period

Active Publication Date: 2022-07-15
SAUDI ARABIAN OIL CO
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this threshold is an undesirable limitation for large-scale basin simulations where fine-grid modeling of HC migration is desired

Method used

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  • Parallel Processing of Invasive Seepage for Large-Scale High-Resolution Simulation of Secondary Oil and Gas Migration
  • Parallel Processing of Invasive Seepage for Large-Scale High-Resolution Simulation of Secondary Oil and Gas Migration
  • Parallel Processing of Invasive Seepage for Large-Scale High-Resolution Simulation of Secondary Oil and Gas Migration

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Experimental program
Comparison scheme
Effect test

test Embodiment 1

[0151] The model used consisted of 135 grid cells in the x direction and 135 grid cells in the y direction with 82 formations, resulting in a model of grid cells of size 1,494,450. The area grid size is 2 kilometers (km) in both the x and y directions, covering a surface area of ​​73,000 square kilometers (km). 2 ). Simulation runs began with the first stratigraphic deposition 550 million years ago (Ma) and used known kinetic models. The facies of the model are simplified to use only two types; one rock type for the seal and the other for the rest of the formation.

[0152] go to Figure 8 , Figure 8 is an image 800 showing the results after running the Test Case 1 model on a single compute core, according to the implementation. As shown, the transition ratio color ranges from red (eg, 805) to light blue (eg, 810), indicating larger to smaller transitions, respectively. The conversion ratio indicates how much of the kerogen has reacted and matured to produce HC. Dark an...

test Embodiment 2a

[0156] Test Case 2a: Coarse Mesh Model

[0157] The second model consists of 535 grid cells in the x-direction and 505 grid cells in the y-direction with 82 formations, resulting in a model of grid cells of size 22,154,350. The face mesh size is 2km in both the x and y directions, covering a surface area of ​​1,080,700km 2 . Similar to Test Case 1, the simulation run started with the first stratigraphic deposition at 550 Ma and used the same kinetic model. Again, the facies of the model are simplified to use only two types: 1) one rock type for the seal and 2) another rock type for the rest of the formation.

[0158] back to Figure 12 , Figure 12 is an image 1200 showing the results from a serial run of test case 2a according to the implementation. The dark green grid cells (eg grid cells at 1205 and 1210) show accumulation saturation (volume). Colors ranging from red (eg, 1215) to blue (eg, 1220) show transition ratios as previously described.

[0159] The use of a l...

test Embodiment 2b

[0164] Test Case 2b: Fine Mesh Model

[0165] To show that this method can be used to run very large-scale high-resolution basin models, the grid size of test case 2b is refined to have 4,273 grid cells in the x-direction and 4,033 grid cells in the y-direction, There are 82 strata in it, resulting in a model with a size of 1,413,106,738 grid cells. All other characteristics of the model remain the same, and the model runs on 1,000 compute cores using 14 processors per compute node.

[0166] go to Figure 15 , Figure 15 is an image 1500 showing the result of running the model of test case 2b using 1000 compute cores according to the implementation. It can be seen that the indicated trap locations correspond to the coarse mesh test case 2a model ( Figure 14B )quite. However, many of the smaller indicated traps that were suspected to be artifacts due to the low-resolution coarse grid in test case 2a disappeared in test case 2b. In one implementation, the parallel simulat...

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Abstract

A parallel approach to hydrocarbon (HC) migration and accumulation is applied to basin data to determine migration paths and traps for high-resolution petroleum system modeling. In parallel, it is determined that HC has been drained from source rock associated with a plurality of grid cells divided into one or more subdomains. Identify potential trap peaks within multiple grid cells. An intrusive percolation (IP) process was performed until the HC ceased to migrate after reaching multiple trapped peaks. Determine if grid cells containing HC contain excess HC volume. The accumulation process is performed to model the filling of HC at the traps associated with the identified geopotential trap peaks. The list of trap boundary cells is updated together with the HC potential values ​​in parallel. Trap filling was terminated when the excess HC was depleted or the overflow point was reached.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS [0002] This application claims priority to US Provisional Application No. 62 / 524,223, filed June 23, 2017, and US Invention Application No. 16 / 007,175, filed June 13, 2018, the entire contents of which are incorporated by reference in their entirety. here. Background technique [0003] Basin Modeling / Simulation (also known as Petroleum System Modeling / Simulation) tracks the evolution of sedimentary basins and their fluid content over eons of geological time by digitizing the basin model and simulating the associated processes associated with the basin. In recent years, it has become an important tool for exploration geoscientists to predict the type and presence of hydrocarbon (HC) fluids and to assess geological risks before drilling wells for HC fluids. Basin simulators typically include numerical modules for computing: 1) stripping and compaction; 2) pressure calculations; 3) heat flow analysis and kinetics; 4) oil productio...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V99/00
CPCG01V2210/644G01V2210/661G06F2111/10G06F30/20G06F30/28G01V20/00G06F2101/10E21B41/00
Inventor 舒豪·纳伊姆·卡尤姆拉里·西乌-屈恩·冯
Owner SAUDI ARABIAN OIL CO
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