Improved Stochastic Simulation Using Stratigraphic Forward Modeling: A Case Study of the Lithological Distribution of Tide-Dominated Estuary in JE-AW Oil Field, Ecuador

Zhang, Kexin; Hong, Huasheng; Chen, Heping; Wan, Xue-peng; Liu, Huiying; Zhang, Chaoqian; Wang, Yusheng; Guo, Songwei; Meng, Zheng

doi:10.1007/978-981-10-7560-5_34

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…On the other hand, stratigraphic forward modeling methods can be used to test hypotheses about the processes that take place in the system, but there is generally no way to condition their results to hard data. Both methods can be incorporated into any workflow (Michael et al 2010;Zhang et al 2019) so that it can be expected to deliver more realistic and objective results.…”

Section: Introductionmentioning

confidence: 99%

Local Ranking of Geological Conceptual Models in Non-stationary Settings Using Multi-point Geostatistics

2022

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In geomodeling, it is commonly accepted that the distribution of physical properties is controlled by the architecture of geological objects. However, insufficient data and the complexity of earth processes create an ill-posed problem where many architectures are plausible. Consequently, several geologists will produce different geological models for the same location. This contribution proposes a way to objectivize the ranking of those conceptual models by comparing them with hard data, both globally for the whole study region and locally for certain of its sectors. The idea is to extend the multi-point geostatistics direct sampling algorithm to be able to extract data events from different training images, representing several competing geological models, and to record the training image origin of values pasted on simulation grid cells. By tracking the frequency with which every training image is visited, we can rank the likelihood of each geological model. Histograms of the frequency of usage of each training image will provide a global ranking of the several conceptual models, while maps of these frequencies can be used to produce the local rankings. We demonstrate this method in two synthetic fluvial depositional environments where three distinct geological concepts are being proposed, with different abundances of hard data. Results indicate that the proposed method could be a useful tool in defining which geological concept dominates at a particular region and which is the frequency ranking for each training image on that region.

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Section: Introductionmentioning

confidence: 99%

Local Ranking of Geological Conceptual Models in Non-stationary Settings Using Multi-point Geostatistics

2022

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Horizontal Well Placement in High Water Cut Oilfield: A Case of JE Oilfield in X Block in Oriente Basin, Ecuador

Zhang

Hong

Chen

et al. 2020

Springer Series in Geomechanics and Geoengineering

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Digital Analogues to Assess Uncertainty of Layering Methods in Reservoir Modelling

Kozlowski,

Davies

2023

Day 4 Thu, October 05, 2023

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Standard reservoir modeling workflows requires the definition of geostatistical parameters, such as variograms or vertical proportion curves, to guide the definition of facies and other properties away from wells. These parameters can be calculated from local data or be defined based on data collected from digital analogs, outcrops or better understood subsurface assets. The well markers and defined layers in the model grid are the backbone of both well analysis for calculating geostatistical properties and posterior population of properties in 3D. We analyze the pitfalls of this methodology and how the proper use of digital analogs can help us reduce bias. Digital analogs of generic reservoirs were generated using stratigraphic forward simulators. These models, which include temporal relationships of facies distributions in 3D space, can be used to benchmark other approaches to modeling. In our tests, we simulate the deposits of fluvial meandering and progradational margin systems. The layers boundaries from these models represent chronostratigraphic horizons. Extracting synthetic wells from the digital analogs allows the effect of different layering, including proportional and conformable, on the calculation of typical parameters used in reservoir modeling to be quantified. As expected, layering selection can play an important role in the definition of geostatistical parameters, introducing significant bias in the way 3D reservoir models are built. The density of synthetic well data versus the relative size of the geological features represented in the digital analogs is another major factor in the ability to properly sample and parametrize the model. Even when using different layering approaches, the calculated parameters are different from the ones calculated on the digital analogs, since standard methods of layering do not necessarily consider temporal correlations. This raises the question of whether it is possible to correctly assess subsurface uncertainty by performing the standard layering workflow, even when different layering scenarios are considered. Geologically realistic layering, derived from plausible temporal well correlations are required. In this study, we applied a new method for assessing uncertainty in reservoir modeling. Incorporating the right digital analogs can be a first step to improve the way well properties are correlated and the rest of the modeling steps that cascade from this.

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Improved Stochastic Simulation Using Stratigraphic Forward Modeling: A Case Study of the Lithological Distribution of Tide-Dominated Estuary in JE-AW Oil Field, Ecuador

Cited by 3 publications

References 2 publications

Local Ranking of Geological Conceptual Models in Non-stationary Settings Using Multi-point Geostatistics

Local Ranking of Geological Conceptual Models in Non-stationary Settings Using Multi-point Geostatistics

Horizontal Well Placement in High Water Cut Oilfield: A Case of JE Oilfield in X Block in Oriente Basin, Ecuador

Digital Analogues to Assess Uncertainty of Layering Methods in Reservoir Modelling

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