储层流体识别的新方法

利用小波变换方法可以把原始信号分解为一系列载有不同信息的子信号,每种子信号载有储层的特定信息,包括流体、岩性和孔隙结构等。我们开发出了基于小波变换的谱分析方法,利用该方法可以从这些子信号中提取出反映地层流体特性的子信号,通过对该子信号的处理识别出地层所含流体的特性。本文利用该流体识别新方法对某油田的实际测井资料进行了处理,处理结果与试油结果符合得很好,进一步证明了本方法的可靠性和实用性。     

Advance first order second moment (AFOSM) method for single reservoir operation reliability analysis: a case study

Reservoir system reliability is the ability of reservoir to perform its required functions under stated conditions for a specified period of time. In classical method of reservoir system reliability analysis, the operation policy is used in a simple simulation model, considering the historical/synthetic inflow series and a number of physical bounds on a reservoir system. This type of reliability analysis assumes a reservoir system as fully failed or functioning, called binary state assumption. A number of researchers from various research backgrounds have shown that the binary state assumption in the traditional reliability theory is not extensively acceptable. Our approach to tackle the present problem space is to implement the algorithm of advance first order second moment (AFOSM) method. In this new method, the inflow and reservoir storage are considered as uncertain variables. The mean, variance and covariance of uncertain variables are determined using moment values of reservoir state variables. For this purpose, a stochastic optimization model developed based on the constraint state formulation is applied. The proposed model of reliability analysis is used to a real case study in Iran. As a result, monthly probabilities of water allocation were computed from AFOSM method, and the outputs were compared with those from Monte Carlo method. The comparison shows that the outputs from AFOSM method are similar to those from the Monte Carlo method. In term of practical use of this study, the proposed method is appropriate to determine the monthly probability of failure in water allocation without the aid of simulation.     

A neural network based general reservoir operation scheme

Construction of dams and the resulting water impoundments are one of the most common engineering procedures implemented on river systems globally; yet simulating reservoir operation at the regional and global scales remains a challenge in human–earth system interactions studies. Developing a general reservoir operating scheme suitable for use in large-scale hydrological models can improve our understanding of the broad impacts of dams operation. Here we present a novel use of artificial neural networks to map the general input/output relationships in actual operating rules of real world dams. We developed a new general reservoir operation scheme (GROS) which may be added to daily hydrologic routing models for simulating the releases from dams, in regional and global-scale studies. We show the advantage of our model in distinguishing between dams with various storage capacities by demonstrating how it modifies the reservoir operation in respond to changes in capacity of dams. Embedding GROS in a water balance model, we analyze the hydrological impact of dam size as well as their distribution pattern within a drainage basin and conclude that for large-scale studies it is generally acceptable to aggregate the capacity of smaller dams and instead model a hypothetical larger dam with the same total storage capacity; however we suggest limiting the aggregation area to HUC 8 sub-basins (approximately equal to the area of a 60 km or a 30 arc minute grid cell) to avoid exaggerated results.     

A method for coupled arch dam‐foundation‐reservoir seismic behaviour analysis

This paper presents a method for coupled arch dam–foundation–reservoir seismic behaviour analysis. The dam is discretized by finite elements (FE) and the foundation and reservoir are discretized by boundary elements (BE). The opening of contraction joints and the spatial variability of the seismic action is taken into account. The study of Pacoima dam by this method is also presented. The computed results show that no cracks were to be expected due to the vibrations induced during the Feb. 9, 1971 San Fernando earthquake. Copyright © 2000 John Wiley & Sons, Ltd.     

Two solution methods for dynamic game in reservoir operation

During the last decade, a number of models have been developed to consider the conflict in dynamic reservoir operation. Most of these models are discrete dynamic models which are developed based on game theory. In this study, a continuous model of dynamic game and its corresponding solutions are developed for reservoir operation. Two solution methods are used to solve the model of continuous dynamic game, namely the Ricatti equations and collocation methods. The Ricatti equations method is a closed form solution, requiring less computational efforts compared with discrete models. The collocation solution method applies Newton's method or a quasi-Newton method to find the problem solution. These approaches are able to generate operating policies for dynamic reservoir operation. The Zayandeh-Rud river basin in central Iran is used as a case study and the results are compared with alternative water allocation models. The results show that the proposed solution methods are quite capable of providing appropriate reservoir operating policies, while requiring rather short computational times due to continuous formulation of state and decision variables. Reliability indices are used to compare the overall performance of the proposed models. Based on the results from this study, the collocation method leads to improved values of the reliability indices for total reservoir system and utility satisfaction of water users, compared to the Ricatti equations method. This is attributed to the flexible structure of the collocation model. When compared to alternative water allocation models, lower values of reliability indices are achieved by the collocation method.     

Incorporating inflow uncertainty into risk assessment for reservoir operation

An attempt of using stochastic hydrologic technique to assess the intrinsic risk of reservoir operation is made in this study. A stochastic simulation model for reservoir operation is developed. The model consists of three components: synthetic generation model for streamflow and sediment sequences, one-dimensional delta deposit model for sediment transport processes in reservoirs, and simulation model for reservoir operation. This kind of integrated simulation model can be used to simulate not only the inflow uncertainty of streamflow and sedimentation, but also the variation in operation rules of reservoirs. It is herein used for the risk assessment of a reservoir, and the simulation is performed for different operation scenarios. Simulation for the 100-year period of sediment transport and deposition in the river-reservoir system indicates that the navigation risk is much higher than that of hydropower generation or sediment deposition in the reservoir. The risk of sediment deposition at the river-section near the backwater profile is also high thereby the navigation at the river-segment near this profile takes high risk because of inadequate navigation depth.     

A reservoir skeleton-based multiple point geostatistics method

Traditional stochastic reservoir modeling, including object-based and pixel-based methods, cannot solve the problem of reproducing continuous and curvilinear reservoir objects. The paper first dives into the various stochastic modeling methods and extracts their merits, then proposes the skeleton-based multiple point geostatistics (SMPS) for the fluvial reservoir. The core idea is using the skeletons of reservoir objects to restrict the selection of data patterns. The skeleton-based multiple point geostatistics consists of two steps. First, predicting the channel skeleton (namely, channel centerline) by using the method in object-based modeling. The paper proposes a new method of search window to predict the skeleton. Then forecasting the distributions of reservoir objects using multiple point geostatistics with the restriction of channel skeleton. By the restriction of channel centerline, the selection of data events will be more reasonable and the realization will be achieved more really. The checks by the conceptual model and the real reservoir show that SMPS is much better than Sisim (sequential indicator simulation), Snesim (Single Normal Equation Simulation) and Simpat (simulation with patterns) in building the fluvial reservoir model. This new method will contribute to both the theoretical research of stochastic modeling and the oilfield developments of constructing highly precise reservoir geological models.

     

碳酸盐岩裂缝型储层最小二乘偏移成像方法研究

碳酸盐岩裂缝型储层是海相油气储层以及西部探区的重要储层类型之一,多表现为缝洞储集体形态,埋藏深度大,地震反射波信号复杂,波场特征分析困难,因而对其进行高分辨成像具有一定的难度.本文推导并实现了基于最小二乘理论的偏移成像(LSM)方法,并将LSM用于西部探区碳酸盐岩裂缝型储层等效地震地质模型的偏移成像工作,成像结果表明:在理论上,LSM方法能够较好地分辨出埋藏深度在3000 m以下的中深部20 m尺度碳酸盐岩裂缝型储层,在西部碳酸盐岩裂缝型储集体地震勘探中,具有较好的适应性.     

A sample reconstruction method based on a modified reservoir index for flood frequency analysis of non-stationary hydrological series

Flood extremes, affected by climate change and intense human activities, exhibit non-stationary characteristics. As a result, the stationarity assumption of traditional flood frequency analysis (FFA) cannot be satisfied. Generally, the impacts of human activities, especially water conservancy projects (i.e., reservoirs), on extreme flood series are much greater than those of climate change; therefore, new FFA methods must be developed to address the non-stationary flood extremes associated with large numbers of reservoirs. In this study, a new sample reconstruction method is proposed to convert the reservoir-influenced annual maximum flow (AMF) series from non-stationary to stationary, thus warranting the feasibility of the traditional FFA approach for non-stationary cases. To be more specifically, a modified reservoir index (MRI(t)) is proposed and the original non-stationary AMF series are converted to stationary series by multiplying by a scalar factor 1/(1 ? MRI(t)), and thus traditional FFA can be adopted. Besides, Bayesian theory was applied to analyze the effect of uncertainty on the designed reconstructed AMF. As an example, the proposed method was applied to observations from Huangzhuang station located on the Hanjiang River. The original AMF observations from Huangzhuang displayed nonstationarity for the continuous construction of reservoirs in the basin. After applying the new method of sample reconstruction, the original AMF observations became stationary, and the designed AMFs were estimated using the reconstructed series and compared with those estimated based on the original observation series. In addition, Bayesian theory is adopted to quantify the uncertainty of designed reconstructed AMF and provide the expectation of the sampling distribution.     

基于聚类分析方法的砾岩油藏储层类型划分

砾岩油藏由于近物源、多水系和快速多变的沉积环境导致储层岩性复杂多变以及非均质性严重等特点,储层类型的精细划分成为该类油藏二次调整开发的重点和难点.本文以克拉玛依油田六中区克下组砾岩油藏为研究对象,选取密闭取心井岩心分析的物性参数、压汞驱替参数以及微观孔隙结构参数共计12项作为砾岩油藏储层类型划分的参数组合,对比研究了基于划分、基于层次、基于模型和基于密度的4种聚类算法建立的储层划分标准,结果表明基于划分的k-means算法建立的聚类标准最符合实际油藏的地质特征和储层类型的划分精度,内部度量的紧凑性、有效性和分辨性都优于其他三种算法,并且分析了Ⅴ类储层与岩性的对应关系,发现砾岩油藏储层类型受岩性控制的机制非常复杂,岩性相同的储层类型可能呈现出不同的物性和渗流性,而岩性不同的储层类型又可能表现为相同的物性和渗流性,其根本原因是储层非均质性造成的.储层类型与砾岩岩性的有效结合为该区精细开发方案的设计和水淹层的定量评价提供了技术支持.     

Evaluation of stochastic reservoir operation optimization models

This paper investigates the performance of seven stochastic models used to define optimal reservoir operating policies. The models are based on implicit (ISO) and explicit stochastic optimization (ESO) as well as on the parameterization–simulation–optimization (PSO) approach. The ISO models include multiple regression, two-dimensional surface modeling and a neuro-fuzzy strategy. The ESO model is the well-known and widely used stochastic dynamic programming (SDP) technique. The PSO models comprise a variant of the standard operating policy (SOP), reservoir zoning, and a two-dimensional hedging rule. The models are applied to the operation of a single reservoir damming an intermittent river in northeastern Brazil. The standard operating policy is also included in the comparison and operational results provided by deterministic optimization based on perfect forecasts are used as a benchmark. In general, the ISO and PSO models performed better than SDP and the SOP. In addition, the proposed ISO-based surface modeling procedure and the PSO-based two-dimensional hedging rule showed superior overall performance as compared with the neuro-fuzzy approach.     

Including daily constraints in a monthly reservoir operation model for low-flow

In many real-world situations, operation of water resources systems are subject to constraints which are formulated on a daily basis. Since mathematical models (simulation or optimization models) are often developed on a monthly basis to avoid dimensionality problems and to reduce necessary computer time, some degree of approximation is necessary. Two examples are presented in order to show how this approximation can be done:

• •- a reservoir is operated for hydroelectric power production and low-flow augmentation is provided on a daily basis as a function of inflows (Lech River System in Germany)
• •- a reservoir is operated optimally for daily low-flow augmentation at a control gage downstream. The model includes also mandatory releases as constraints (Wupper River System in Germany).

In the first example the daily requirements are part of the constraints in the system, while in the second example the daily constraint concerns directly the objective function. Further, in the first case water can be saved and in the second case more water is needed as compared to calculations on a monthly basis.These examples are presented to show two solutions using envelope curves but other possibilities (i.e., regression analysis, constraints, coefficients) could be considered.     

基于迁移学习的地球物理测井储层参数预测方法研究

随着大数据和机器学习的成熟和推广应用,人工神经网络在地球物理测井预测储层参数中得到重视.本文引入迁移学习进行测井储层参数预测,以孔隙度预测神经网络模型和孔隙度含水饱和度联合预测神经网络模型为基础模型,分别以渗透率及含水饱和度预测作为目标任务进行迁移学习,以提升储层参数预测效果和效率.文中详细阐述了基于迁移学习的测井储层参数预测方法,并使用64口井的测井数据进行储层参数预测效果分析.结果表明,使用迁移学习后,渗透率模型预测效果最高可以提升58.3%;含水饱和度模型预测效果最高可以提升近40%,且最大可以节省60%的计算资源;以孔隙度预测模型为基础模型时更适合使用参数冻结的训练方式,以孔隙度含水饱和度联合预测模型为基础模型时更适合使用参数微调的训练方式.

     

A time-domain induced-polarization method for estimating permeability in a shaly sand reservoir

It is known that the time‐domain induced‐polarization decay curve for a shaly sand reservoir depends on the pore structure of the reservoir, and this curve can be used to estimate permeability, which is a determining factor in making production decisions in the petroleum industry. Compared with NMR logging tools, induced polarization has several advantages, such as a deep depth of investigation and a high signal‐to‐noise ratio. The purpose of this paper is to establish an appropriate model using induced polarization to estimate the permeability. The curve can be modelled as a weighted superposition of exponential relaxations. The plot of weight versus the relaxation time constant is defined as the relaxation time spectrum. Induced‐polarization decay‐curve measurements were performed on 123 samples from the Daqing oilfield using a four‐electrode technique. A singular‐value decomposition method was used to transform the induced‐polarization decay data into a spectrum. Different models to estimate the permeability were discussed. The results of the research indicate that the induced‐polarization measurements greatly improve the statistical significance of permeability correlations. Compared with the traditional forms, Aφ^C and AF^C, the forms, AT^Bφ^C and AT^BF^C, have lower error factors, where T, Φ and F are the geometric mean time constant of the induced‐polarization relaxation time spectrum, the porosity and the resistivity formation factor, respectively, and A, B and C are constants. The mean time constant is the decisive parameter in the permeability estimation and it is not completely independent of the resistivity formation factor. The additional use of the porosity and the resistivity formation factor leads to an appreciable improvement. It is concluded that this new model will make it possible to estimate the permeability of a shaly sand reservoir downhole.     

Rule reduction in fuzzy logic for better interpretability in reservoir operation

Decision‐making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (‘low’, ‘high’, etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering‐based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine‐tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd.     

Intelligent control for modelling of real‐time reservoir operation

This paper presents a new approach to improving real‐time reservoir operation. The approach combines two major procedures: the genetic algorithm (GA) and the adaptive network‐based fuzzy inference system (ANFIS). The GA is used to search the optimal reservoir operating histogram based on a given inflow series, which can be recognized as the base of input–output training patterns in the next step. The ANFIS is then built to create the fuzzy inference system, to construct the suitable structure and parameters, and to estimate the optimal water release according to the reservoir depth and inflow situation. The practicability and effectiveness of the approach proposed is tested on the operation of the Shihmen reservoir in Taiwan. The current M‐5 operating rule curves of the Shihmen reservoir are also evaluated. The simulation results demonstrate that this new approach, in comparison with the M‐5 rule curves, has superior performance with regard to the prediction of total water deficit and generalized shortage index (GSI). Copyright © 2001 John Wiley & Sons, Ltd.     

Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation

Genetic algorithms (GAs) are well known optimization methods. However, complicated systems with high dimensional variables, such as long-term reservoir operation, usually prevent the methods from reaching optimal solutions. This study proposes a multi-tier interactive genetic algorithm (MIGA) which decomposes a complicated system (long series) into several small-scale sub-systems (sub-series) with GA applied to each sub-system and the multi-tier (key) information mutually interacts among individual sub-systems to find the optimal solution of long-term reservoir operation. To retain the integrity of the original system, over the multi-tier architecture, an operation strategy is designed to concatenate the primary tier and the allocation tiers by providing key information from the primary tier to the allocation tiers when initializing populations in each sub-system. The Shihmen Reservoir in Taiwan is used as a case study. For comparison, three long-term operation results of a sole GA search and a simulation based on the reservoir rule curves are compared with that of MIGA. The results demonstrate that MIGA is far more efficient than the sole GA and can successfully and efficiently increase the possibility of achieving an optimal solution. The improvement rate of fitness values increases more than 25%, and the computation time dramatically decreases 80% in a 20-year long-term operation case. The MIGA with the flexibility of decomposition strategies proposed in this study can be effectively and suitably used in long-term reservoir operation or systems with similar conditions.     

A new energy density calculation method for reservoir delineation using seismic low-frequency signal

In comparison to high-frequency signals, low-frequency seismic signals suffer less from scattering and intrinsic attenuation during wave propagation, penetrate deeper strata and thus can provide more energy information related to the hydrocarbon reservoirs. Based on the asymptotic representation for the frequency-dependent reflections in the fluid-saturated pore-elastic media, we first derive a novel equation of the reservoir energy density and present an efficient workflow to calculate the reservoir energy density using low-frequency seismic data. Then, within a low-frequency range (from 1 to 30 Hz), we construct an objective function to determine the optimal frequency, using the energy densities calculated from the post-stack seismic traces close to the wells. Next, we can calculate the reservoir energy density using the instantaneous spectra of optimal frequency at the low-frequency end of the seismic spectrum. Tests on examples for synthetic and field data demonstrate that the proposed reservoir energy density can produce high-quality images for the fluid-saturated reservoirs, and it produces less background artefacts caused by elastic layers. This method provides a new way to detect the location of hydrocarbon reservoirs and characterize their spatial distribution.