## Reservoir Modeling(油藏建模)研究综述

Reservoir Modeling 油藏建模 - Interpolation of porosity and permeability data with minimum error and high accuracy is, therefore, essential in reservoir modeling.^{[1]}A half-iterative process between reservoir modeling and fracture modeling was begun, and an actual reservoir pressure-depletion map was created on a sector basis.

^{[2]}The complexity of reservoir modeling and the use of numerical optimization to history match the laboratory data have shown the importance of concave relative permeability curves.

^{[3]}Net pay is the key parameter in reserve estimation, reservoir modeling, production planning, well test interpretation and selection of perforation intervals.

^{[4]}Considering the capital intensity and the importance of the project for the organization of surfactant-polymer flooding, PJSC Tatneft pay great attention to high-quality preparatory work, including laboratory filtration studies, the results of which were then used during the reservoir modeling of the process to form the most reliable feasibility study of the technology.

^{[5]}It inferred active development of geomodeling and occurrence of first software to reservoir modeling in 1980-s.

^{[6]}Therefore, the domains of artificial intelligence and machine learning (ML) were used to alleviate this computational challenge by creating a new class of reservoir modeling, namely smart proxy modeling (SPM).

^{[7]}Besides this fact, the heterogeneity of the secondary pore distribution could cause unstable test results of permeability, which can bring out significant uncertainty in reservoir modeling if the problem is ignored.

^{[8]}The current approach of evaluating the WAG process, using reservoir modeling, is a very time-consuming and costly task.

^{[9]}Streamlines have been used for reservoir modeling and flow visualization in the petroleum industry and in computational fluid dynamics.

^{[10]}Moreover, the paper offers an integrated volumetric and reservoir modeling for estimation of gas-in-place (GIP) in the studied interval of the Muerto formation.

^{[11]}Finally, practical issues of data interoperability between different pieces of software are discussed and tips on implementation of the obtained trends in reservoir modeling are given.

^{[12]}Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming.

^{[13]}The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry.

^{[14]}Therefore, this paper is proposed to provide an alternative solution to identify the presence of the fractures, classify them into the fractured quality related flowability, and distribute them vertically within the well interval and propose a lateral distribution method for reservoir modeling.

^{[15]}While the petroleum industry has been the historical motivation for supercomputing in the Arab World, with its workloads of seismic imaging and reservoir modeling, the attraction today is universal.

^{[16]}With large amounts of simultaneous data, like inverted seismic data in reservoir modeling, negative effects of Monte Carlo errors in straightforward ensemble-based data assimilation (DA) are enhanced, typically resulting in underestimation of parameter uncertainties.

^{[17]}The predicted RP and CP relationship can be generated and applied in history matching and reservoir modeling.

^{[18]}Primary drainage as well as corresponding imbibition and/or secondary drainage capillary pressure curves are averaged to establish a saturation table for each rock type region in reservoir modeling.

^{[19]}First, the porosity and permeability distribution of the reservoir matrix are established based on reservoir modeling.

^{[20]}Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation.

^{[21]}This paper addresses the problem of reservoir modeling in the context of large scale developments, in which multiple wells with hundreds of fractures are placed in a formation.

^{[22]}These parameters can be a target for subsurface investigation, reservoir modeling and hydraulic stimulation at a later stage.

^{[23]}Data analysis was performed using hybrid digital models based on geological and reservoir modeling and a simplified physical reservoir model, involving machine-learning algorithms underlain by neural networks.

^{[24]}Such variation in the fluvial styles would serve as a warning as to a rather simplistic assumptions on the channel scale and styles during reservoir modeling.

^{[25]}In recent years, great advances have been achieved in resource assessment, reservoir modeling, geology, geophysics, geochemistry, and other areas of geosciences related to geothermal energy.

^{[26]}The investigation entails careful sample preparation and cleaning of mini-plugs, operation with reservoir fluids, wettability restoration, centrifuge wettability testing cycles, repeated sample scanning and image analysis, parametrization of wettability and digital rocks simulation for input into reservoir modeling.

^{[27]}For petroleum exploration and development, inter-well formation property estimation is very important since it is the foundation of further reservoir modeling and simulation.

^{[28]}This work presents the implementation of an uncertainty workflow in reservoir management with a focus on reservoir modeling and simulation.

^{[29]}However, the best description of the WRS in terms of location of a considered outcrop, physical conditions under which the WRS developed and their lateral variability, type of substrate, as well as its practical significance for reservoir modeling is achieved with a combination of the two criteria.

^{[30]}Calibrating these models in a history-matching procedure normally requires integration with geostatistical techniques (Big Loop, where the history matching is integrated to reservoir modeling) for proper model characterization.

^{[31]}It is essential that these features are quantified and mitigated as a prerequisite for robust application of rule-based aggradational lobe methods for reservoir modeling.

^{[32]}This study focuses on site-specific geologic characterization, reservoir modeling, and CO2 storage resource assessment ( capacity) of a depleted oil and gas field located on the inner continental shelf of the Gulf of Mexico, the High Island 1 0L Field.

^{[33]}The data and the tuned model find applications in the reservoir modeling of solvent-aided thermal recovery of bitumen and heavy oil.

^{[34]}In addition to the current application, the statistical method may serve as useful tool for improved well log analysis, well-to-well correlation and reservoir modeling on larger scales.

^{[35]}Our intent is to detail a workflow that can facilitate mapping of present-day good reservoir quality carbonate mound geometries to enable their characterization from a seismic perspective and to allow assessment of their spatial distribution for the purposes of reservoir modeling during the exploration and appraisal stages.

^{[36]}That is why, the search for alternative approaches of reservoir modeling, which ensure prompt obtaining realistic forecasting of its development, was relevant.

^{[37]}This illustrates the significance of stratigraphic uncertainties in reservoir modeling and the role of automatic methods to help assess and reduce these uncertainties.

^{[38]}This study is significant in understanding the subsurface behavior during drilling, reservoir modeling, future well stimulation and production optimization.

^{[39]}, reservoir modeling).

^{[40]}The numerical simulation of multicomponent compressible flow in porous media is an important research topic in reservoir modeling.

^{[41]}Logging records are first used for single-well evaluations and then extended to fieldwide resource evaluation and reservoir modeling.

^{[42]}Summary A major challenge in reservoir modeling is the accurate representation of lithofacies in a defined framework to honor geologic knowledge and available subsurface data.

^{[43]}This chapter presents an introduction to reservoir modeling, including the aims, principles, and general workflows.

^{[44]}

因此，以最小误差和高精度插值孔隙度和渗透率数据对于储层建模至关重要。

^{[1]}开始了储层建模和裂缝建模之间的半迭代过程，并以扇区为基础创建了实际的储层压力-衰竭图。

^{[2]}储层建模的复杂性和使用数值优化来匹配实验室数据表明了凹形相对渗透率曲线的重要性。

^{[3]}净产值是储量估算、油藏建模、生产计划、试井解释和射孔间隔选择的关键参数。

^{[4]}考虑到资金密集度和该项目对组织表面活性剂-聚合物驱的重要性，PJSC Tatneft 非常重视高质量的准备工作，包括实验室过滤研究，然后将其结果用于该过程的油藏建模形成最可靠的技术可行性研究。

^{[5]}它推断在 1980 年代地质建模的积极发展和第一个油藏建模软件的出现。

^{[6]}因此，人工智能和机器学习 (ML) 领域被用来通过创建一类新的油藏建模，即智能代理建模 (SPM) 来缓解这一计算挑战。

^{[7]}除此之外，次生孔隙分布的非均质性会导致渗透率测试结果不稳定，如果忽视这一问题，会给储层建模带来很大的不确定性。

^{[8]}目前使用油藏建模评估 WAG 过程的方法是一项非常耗时且成本高昂的任务。

^{[9]}流线已被用于石油工业和计算流体动力学中的油藏建模和流动可视化。

^{[10]}此外，该论文提供了一个集成的体积和储层建模，用于估计 Muerto 地层研究层段中的就地天然气 (GIP)。

^{[11]}最后，讨论了不同软件之间数据互操作性的实际问题，并给出了在油藏建模中实施所获得趋势的技巧。

^{[12]}用于预测页岩储层生产力的储层建模是相当不确定且耗时的。

^{[13]}岩石无侧限抗压强度 (UCS) 是石油工业中地质力学和储层建模的关键参数之一。

^{[14]}因此，本文提出了一种替代解决方案来识别裂缝的存在，将其分类为与裂缝质量相关的流动性，并在井段内垂直分布，并提出一种用于储层建模的横向分布方法。

^{[15]}虽然石油行业一直是阿拉伯世界超级计算的历史动力，其地震成像和储层建模的工作量，但今天的吸引力是普遍的。

^{[16]}随着大量同步数据，如储层建模中的反演地震数据，直接基于集合的数据同化 (DA) 中的蒙特卡罗误差的负面影响会增强，通常会导致参数不确定性的低估。

^{[17]}预测的 RP 和 CP 关系可以生成并应用于历史匹配和储层建模。

^{[18]}对一次排水以及相应的渗吸和/或二次排水毛管压力曲线进行平均，以建立储层建模中每个岩石类型区域的饱和度表。

^{[19]}首先，基于储层建模建立储层基质的孔隙度和渗透率分布。

^{[20]}岩石孔隙度是地层评价、储层建模和石油储量估算的重要参数。

^{[21]}本文解决了大规模开发背景下的储层建模问题，其中具有数百个裂缝的多口井被放置在一个地层中。

^{[22]}这些参数可以成为后期地下调查、油藏建模和水力增产的目标。

^{[23]}使用基于地质和储层建模的混合数字模型以及简化的物理储层模型进行数据分析，其中涉及神经网络基础的机器学习算法。

^{[24]}河流样式的这种变化将作为一个警告，即在储层建模期间对河道规模和样式的相当简单的假设。

^{[25]}近年来，在资源评价、储层建模、地质学、地球物理、地球化学等与地热能相关的地球科学领域取得了很大进展。

^{[26]}调查需要仔细的样品制备和小型塞子的清洁、储层流体的操作、润湿性恢复、离心机润湿性测试循环、重复的样品扫描和图像分析、润湿性的参数化和用于输入储层建模的数字岩石模拟。

^{[27]}对石油勘探开发而言，井间地层性质估计是进一步油藏建模和模拟的基础。

^{[28]}这项工作介绍了在油藏管理中实施不确定性工作流程，重点是油藏建模和模拟。

^{[29]}然而，就所考虑的露头位置、WRS 发育的物理条件及其横向变异性、基质类型以及其对储层建模的实际意义而言，WRS 的最佳描述是通过将两者结合起来实现的标准。

^{[30]}在历史匹配过程中校准这些模型通常需要与地质统计技术（Big Loop，其中将历史匹配集成到储层建模）集成以进行正确的模型表征。

^{[31]}将这些特征量化和减轻是必不可少的，这是基于规则的加积叶方法在储层建模中稳健应用的先决条件。

^{[32]}本研究侧重于位于墨西哥湾内陆架高岛 1 0L 油田的枯竭油气田的特定地点地质特征、储层建模和 CO2 储存资源评估（容量）。

^{[33]}数据和调整后的模型在沥青和重油的溶剂辅助热采油藏建模中得到应用。

^{[34]}除了当前的应用之外，统计方法还可以作为改进测井分析、井间相关性和大规模储层建模的有用工具。

^{[35]}我们的目的是详细说明一个工作流程，该工作流程可以促进绘制当今良好的储层质量碳酸盐丘几何形状，以便从地震角度对其进行表征，并允许评估其空间分布，以便在勘探和评估阶段进行储层建模。

^{[36]}这就是为什么寻找储层建模的替代方法以确保迅速获得对其发展的现实预测的原因。

^{[37]}这说明了地层不确定性在储层建模中的重要性以及自动方法在帮助评估和减少这些不确定性方面的作用。

^{[38]}这项研究对于了解钻井过程中的地下行为、储层建模、未来的油井增产和生产优化具有重要意义。

^{[39]}，油藏建模）。

^{[40]}多孔介质中多分量可压缩流动的数值模拟是油藏建模的重要研究课题。

^{[41]}测井记录首先用于单井评价，然后扩展到全场资源评价和储层建模。

^{[42]}总结 储层建模的一个主要挑战是在定义的框架中准确表示岩相，以尊重地质知识和可用的地下数据。

^{[43]}本章介绍油藏建模，包括目标、原理和一般工作流程。

^{[44]}

## 3d Reservoir Modeling 3d 油藏建模

Accounting for facies uncertainty is not a mere exercise in style, rather it is fundamental for the purpose of understanding the reliability of the classification results, and it also represents a critical information for 3D reservoir modeling and/or seismic characterization processes.^{[1]}The developed SHM and the determined FWLs can be used for 3D reservoir modeling, in situ gas volumetric analysis and Sw calculation in undrilled areas to formulate a field development plan and fracturing program.

^{[2]}It is necessary to first decouple the debiasing of well data from the 3D reservoir modeling.

^{[3]}In this paper, Chang 8 layers in this area are studied by using reservoir sedimentology, logging geology, petroleum geology, petrophysics, 3D reservoir modeling and numerical simulation and laboratory analysis techniques.

^{[4]}

考虑相不确定性不仅仅是一种风格练习，而是理解分类结果可靠性的基础，它还代表了 3D 储层建模和/或地震表征过程的关键信息。

^{[1]}开发的SHM和确定的FWL可用于未钻区域的3D储层建模、原位气体体积分析和Sw计算，以制定油田开发计划和压裂方案。

^{[2]}有必要首先将井数据的去偏与 3D 油藏建模分离。

^{[3]}本文采用储层沉积学、测井地质、石油地质、岩石物理、3D储层建模与数值模拟及实验室分析等技术，对该区长8层进行了研究。

^{[4]}

## Static Reservoir Modeling 静态油藏建模

Results indicated that static reservoir modeling adequately captured reservoir geometry and spatial properties distribution.^{[1]}To this end, we corroborated, in this study, static reservoir modeling with petroleum system analysis workflows, to better characterize the Cenomanian fluvio-marine reservoir, sandstones of Bahariya Formation, at Bed-2 Field, Abu Gharadig basin (Western Desert, Egypt).

^{[2]}Static reservoir modeling adequately and precisely defines the reservoir framework (geometry) and architecture (property).

^{[3]}

结果表明，静态油藏建模充分捕捉了油藏几何形状和空间特性分布。

^{[1]}为此，我们在本研究中证实了使用石油系统分析工作流程的静态油藏建模，以更好地表征阿布加拉迪格盆地（埃及西部沙漠）Bed-2 油田的 Cenomanian 河海油藏、Bahariya 组砂岩.

^{[2]}静态油藏建模充分而精确地定义了油藏框架（几何）和架构（属性）。

^{[3]}

## Dynamic Reservoir Modeling 动态油藏建模

Studies were also done by incorporating the static and dynamic reservoir modeling data.^{[1]}Water saturation in porous media plays a significant role in static, dynamic reservoir modeling and petroleum reserves calculations.

^{[2]}History matching is a critical step for dynamic reservoir modeling to establish a reliable, predictive model.

^{[3]}

还通过结合静态和动态储层建模数据进行了研究。

^{[1]}多孔介质中的含水饱和度在静态、动态储层建模和石油储量计算中起着重要作用。

^{[2]}nan

^{[3]}

## Geostatistical Reservoir Modeling

Geostatistical Reservoir Modeling, Oxford University Press, New York, 2002; Pyrcz and Deutsch 2014; Rubio, R.^{[1]}Geostatistical reservoir modeling is an interpolation technique that allows geoscientists to generate different petroleum reservoir models by integrating well logs and 3D seismic data.

^{[2]}

Geostatistical Reservoir Modeling，牛津大学出版社，纽约，2002； Pyrcz 和德意志 2014；卢比奥，R.

^{[1]}地质统计油藏建模是一种插值技术，它允许地球科学家通过整合测井日志和 3D 地震数据来生成不同的石油油藏模型。

^{[2]}

## Driven Reservoir Modeling

TDM is a data-driven reservoir modeling approach under the realm of subsurface analytics technology that uses AI and machine learning to develop full-field reservoir models based on measurements rather than solutions of governing equations.^{[1]}CONFIRMATION OF DATA-DRIVEN RESERVOIR MODELING USING NUMERICAL RESERVOIR SIMULATION Al Hasan Mohamed Al Haifi Data driven reservoir modeling, also known as Top-Down Model (TDM), is an alternative to the traditional numerical reservoir simulation technique.

^{[2]}

TDM 是地下分析技术领域下的一种数据驱动的油藏建模方法，它使用人工智能和机器学习来开发基于测量而不是控制方程解的全油田油藏模型。

^{[1]}使用数值油藏模拟确认数据驱动的油藏建模 Al Hasan Mohamed Al Haifi 数据驱动的油藏建模，也称为自顶向下模型 (TDM)，是传统数值油藏模拟技术的替代方法。

^{[2]}

## Fractured Reservoir Modeling

Since different shape-factors can lead to totally different reservoir behavior, selection of the appropriate shape-factor value is critical for accurate fractured reservoir modeling.^{[1]}The workflow consists of several steps to reduce uncertainties in the fractured reservoir modeling, both in estimating (i) the paleo-geometry of the main structures using 3D reconstruction techniques and (ii), the paleo-tectonic stresses using fracture-based stress inversion technology, both steps being essential to comprehensive geomechanical simulations through geological time.

^{[2]}

由于不同的形状因子会导致完全不同的储层行为，因此选择合适的形状因子值对于准确的裂缝性储层建模至关重要。

^{[1]}该工作流程包括几个步骤，以减少裂缝储层建模中的不确定性，包括（i）使用 3D 重建技术估计主要结构的古几何和（ii）使用基于裂缝的应力反演技术估计古构造应力，这两个步骤对于通过地质时间进行综合地质力学模拟都是必不可少的。

^{[2]}

## Numerical Reservoir Modeling

We show from analysis of generated data, using both numerical reservoir modeling and analytical derivations for a radial flow system, that fall-off tests analyzed using the cartesian G function can generate false indications of fracture closing where in fact, the entire injection has been based on radial flow homogeneous injection systems.^{[1]}Predictions based upon a new technique for modeling wave propagation in a poroelastic medium containing an arbitrary number of fluids, coupled with multicomponent numerical reservoir modeling at Cranfield, reproduce the general pattern of observed seismic amplitude changes and travel time shifts.

^{[2]}

我们使用数值储层建模和径向流系统的分析推导对生成的数据进行分析，表明使用笛卡尔 G 函数分析的脱落测试可能会产生裂缝闭合的错误指示，而事实上，整个注入都是基于关于径向流均质喷射系统。

^{[1]}基于对包含任意数量流体的多孔弹性介质中的波传播进行建模的新技术的预测，再加上 Cranfield 的多分量数值储层建模，再现了观测到的地震幅度变化和走时偏移的一般模式。

^{[2]}

## reservoir modeling study 油藏模型研究

Such integrated reservoir modeling studies, however, can be time-consuming and do not necessarily enable quicker decision-making around operational activities.^{[1]}A number of reservoir modeling studies have been performed for major reservoirs, however, there are still challenges to be addressed.

^{[2]}Also the data obtained may be used as basis for future reservoir modeling studies in the region.

^{[3]}This paper presents a discussion of the reservoir modeling study that was conducted for history-matching oil production and CO2 injection responses, forecasting CO2 plume migration and estimating associated storage to characterize the closed, depleted Dover-33 reef which has undergone extensive primary and secondary production.

^{[4]}This experiment included polymer synthesis, polymer modification, rheological measurement of the product and reservoir modeling study.

^{[5]}

然而，这种综合油藏建模研究可能很耗时，并且不一定能够围绕运营活动更快地做出决策。

^{[1]}已经对主要储层进行了许多储层建模研究，但是仍然存在有待解决的挑战。

^{[2]}获得的数据也可用作该地区未来储层建模研究的基础。

^{[3]}nan

^{[4]}该实验包括聚合物合成、聚合物改性、产品的流变测量和油藏建模研究。

^{[5]}

## reservoir modeling approach 油藏建模方法

In this study, multiple-point facies geostatistics based on the SNESIM algorithm integrated with the seismic modeling technique is used as an efficient reservoir modeling approach for lithofacies modeling of the fluvial Tipam formation in the Upper Assam Basin, India.^{[1]}This paper compares multiple reservoir modeling approaches for history-matching oil production and CO2 injection responses, and estimating associated storage, to characterize these small spatial footprint depleted reef reservoirs.

^{[2]}TDM is a data-driven reservoir modeling approach under the realm of subsurface analytics technology that uses AI and machine learning to develop full-field reservoir models based on measurements rather than solutions of governing equations.

^{[3]}In order to identify and model water supply and transfers at the ecosystem scale, this study combines a range of hydrological, geochemical and reservoir modeling approaches.

^{[4]}

在这项研究中，基于 SNESIM 算法与地震建模技术相结合的多点相地质统计学被用作印度上阿萨姆盆地蒂帕姆河流地层岩相建模的有效储层建模方法。

^{[1]}本文比较了历史匹配石油生产和二氧化碳注入响应的多种储层建模方法，并估计相关存储，以表征这些小空间足迹耗尽的珊瑚礁储层。

^{[2]}TDM 是地下分析技术领域下的一种数据驱动的油藏建模方法，它使用人工智能和机器学习来开发基于测量而不是控制方程解的全油田油藏模型。

^{[3]}nan

^{[4]}

## reservoir modeling purpose 油藏建模目的

It is also shown how this provides fundamental information for perforation strategy optimization and reservoir modeling purposes in such carbonate rocks.^{[1]}The complete methodology, first validated with ad-hoc synthetic case studies, has been then applied to two real cases, where the petrophysical uncertainty has been required for reservoir modeling purposes.

^{[2]}The results obtained from the approach has exhibited great advantages in terms of improvement in the quality and flexibility of the model, reduction of working time and generation of a single final model that can be adapted to describe and evaluate the reservoir, thus, an integrated approach is necessary for reservoir modeling purposes and this is beneficial in describing nature of reservoir as well as evaluating other reservoir with the similar properties.

^{[3]}

还展示了这如何为此类碳酸盐岩中的射孔策略优化和储层建模目的提供基本信息。

^{[1]}完整的方法首先通过临时合成案例研究进行验证，然后已应用于两个实际案例，其中为了储层建模目的需要岩石物理不确定性。

^{[2]}从该方法获得的结果在提高模型的质量和灵活性、减少工作时间和生成可用于描述和评估储层的单一最终模型方面显示出巨大的优势，因此，一种综合方法对于油藏建模目的，这对于描述油藏的性质以及评估具有相似性质的其他油藏很有帮助。

^{[3]}

## reservoir modeling software 油藏建模软件

To evaluate this concept, we performed numerical simulations of cyclic natural gas injection into unconventional shale reservoirs using cmg-gem commercial reservoir modeling software.^{[1]}Historical trends, including fiber optics gauges ones, are visualized and data sets could be retrieved using a fast and user-friendly software that enables data import into interpretation and reservoir modeling software.

^{[2]}Commercial tools to model and simulate structural uncertainty have become easily available, either as stand-alone applications or fully integrated in widely used reservoir modeling software packages.

^{[3]}

为了评估这一概念，我们使用 cmg-gem 商业油藏建模软件对非常规页岩油藏的循环天然气注入进行了数值模拟。

^{[1]}历史趋势（包括光纤仪表的趋势）被可视化，并且可以使用快速且用户友好的软件检索数据集，该软件可以将数据导入解释和油藏建模软件。

^{[2]}用于建模和模拟结构不确定性的商业工具已变得很容易获得，无论是作为独立应用程序还是完全集成到广泛使用的油藏建模软件包中。

^{[3]}

## reservoir modeling workflow

We present a case study in the Lubina and Montanazo mature oil fields (Western Mediterranean) in which the structural uncertainty in the seismic interpretation of faults and horizons has been captured using modern reservoir modeling workflows.^{[1]}We present a case study in the Lubina and Montanazo mature oil fields (Western Mediterranean) in which the structural uncertainty in the seismic interpretation of faults and horizons has been captured using modern reservoir modeling workflows.

^{[2]}

我们介绍了 Lubina 和 Montanazo 成熟油田的案例研究 （西地中海）地震中的结构不确定性 断层和层位的解释已经使用现代 油藏建模工作流程。

^{[1]}我们在 Lubina 和 Montanazo 成熟油田（西地中海）提出了一个案例研究，其中使用现代储层建模工作流程捕获了断层和层位地震解释中的结构不确定性。

^{[2]}

## reservoir modeling method

As one emerging reservoir modeling method, cycle reservoir with regular jumps (CRJ) provides one effective tool for many time series analysis tasks such as ship heave motion prediction.^{[1]}Both a bench-mark example and a case study of Beijing city geothermal field are presented to demonstrate the reasonability and efficiency of the proposed reservoir modeling method.

^{[2]}

作为一种新兴的油藏建模方法，具有规则跳跃的循环油藏（CRJ）为船舶起伏运动预测等许多时间序列分析任务提供了一种有效的工具。

^{[1]}通过北京城市地热田的基准示例和案例研究，证明了所提出的储层建模方法的合理性和有效性。

^{[2]}

## reservoir modeling adequately

Results indicated that static reservoir modeling adequately captured reservoir geometry and spatial properties distribution.^{[1]}Static reservoir modeling adequately and precisely defines the reservoir framework (geometry) and architecture (property).

^{[2]}

结果表明，静态油藏建模充分捕捉了油藏几何形状和空间特性分布。

^{[1]}静态油藏建模充分而精确地定义了油藏框架（几何）和架构（属性）。

^{[2]}