Flow Unit Classification Research Articles

Applying machine learning for hydraulic flow unit classification and permeability prediction: case study from carbonate reservoir in the Southern part, Song Hong basin

Machine learning (ML) is an artificial intelligence (AI) that enables computer systems to classify, cluster, identify, and analyze vast and complex sets of data while eliminating the need for explicit instructions and programming. For decades, machine learning has become helpful for complex reservoir characterization such as carbonate reservoirs. Permeability prediction from well logs is a significant challenge, especially when the core data is rarely available due to its high cost. In this study, we aimed to bridge this gap by demonstrating the practical application of integrating Hydraulic Flow Units (HFU) and machine learning methods. Our goal was to provide a reliable estimation of permeability using core and wireline logging data in the complex Middle Miocene carbonate reservoir of the CX gas field in the southern part of the Song Hong basin. In the first step, due to the reservoir’s heterogeneity, the core plug dataset was classified into 5 HFUs based on the flow zone indicators (FZI) concept from the modified Kozeny-Carman equation using unsupervised machine learning - K-means method. The porosity - permeability for each HFU was defined after HFU clustering. In the second step, we designed three different workflows to predict permeability and HFU using supervised machine learning from a combination of core and log data. These workflows were rigorously test and compared with the core data. The most accurate result was chosen as the base, providing a high confidence level in our predictions’ reliability.

Flow unit classification and characterization with emphasis on the clustering methods: a case study in a highly heterogeneous carbonate reservoir, eastern margin of Dezful Embayment, SW Iran

Previous attempts to classify flow units in Iranian carbonate reservoirs, based on porosity and permeability, have faced challenges in correlating the rock's pore size distribution with the capillary pressure profile. The innovation of this study highlights the role of clustering techniques, such as Discrete Rock Type, Probability, Global Hydraulic Element, and Winland's Standard Chart in enhancing the reservoir's rock categorization. These techniques are integrated with established flow unit classification methods. They include Lucia, FZI, FZI*, Winland R35, and the improved stratigraphic modified Lorenz plot. The research accurately links diverse pore geometries to characteristic capillary pressure profiles, addressing heterogeneity in intricate reservoirs. The findings indicate that clustering methods can identify specific flow units, but do not significantly improve their classification. The effectiveness of these techniques varies depending on the flow unit classification method employed. For instance, probability-based methods yield surpassing results for low-porosity rocks when utilizing the FZI* approach. The discrete technique generates the highest number of flow unit classes but provides the worst result. Not all clustering techniques reveal discernible advantages when integrated with the FZI method. In the second part, the study creatively suggests that rock classification can be achieved by concurrently clustering irreducible water saturation (SWIR) and porosity in unsuccessful flow unit delineation cases. The SWIR log was estimated by establishing a smart correlation between porosity and SWIR in the pay zone, where water saturation and SWIR match. Then, the estimated saturation was dispersed throughout the reservoir. Subsequently, the neural network technique was employed to cluster and propagate the three finalized flow units. This methodology is an effective recommendation when conventional flow unit methods fail. The study also investigates influential factors causing the failure of flow unit classification methods, including pore geometry, oil wettability, and saturation in heterogeneous reservoirs.

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Integrated Core and Log Data to Determine the Reservoir Flow Unit and Rock Facies for Mishrif Formation in South Eastern Iraq

This work represents study the rock facies and flow unit classification for the Mishrif carbonate reservoir in Buzurgan oil Field, which located n the south eastern Iraq, using wire line logs, core samples and petrophysical data (log porosity and core permeability). Hydraulic flow units were identified using flow zone indicator approach and assessed within each rock type to reach better understanding of the controlling role of pore types and geometry in reservoir quality variations. Additionally, distribution of sedimentary facies and Rock Fabric Number along with porosity and permeability was analyzed in three wells (BU-1, BU-2, and BU-3). The interactive Petrophysics - IP software is used to assess the rock fabric number, flow zones, and reservoir quality index. The Mishrif Formation has four distinct rock types and groups that have been recognized using the approach. The first rock type represents a poor reservoir quality (mud-dominated fabrics) with rock-fabric numbers equals to 2.5-4, the second rock type reveals a moderate reservoir quality (wackestone-Packstone Microfacies), the third rock type depicts a good reservoir quality (grain-dominated packstone) with rock-fabric numbers equals to 1.5-2.5 and 0.5-1.5.

Reservoir Characterization and Rock Typing of Carbonate Reservoir in the Southeast of Iraq

Flow unit and reservoir rock type identification in carbonates are difficult due to the intricacy of pore networks caused by facies changes and diagenetic processes. On the other hand, these classifications of rock type are necessary for understanding a reservoir and predicting its production performance in the face of any activity. The current study focuses on rock type and flow unit classification for the Mishrif reservoir in Iraq's southeast and the study is based on data from five wells that penetrate it. Integration of several methods was used to determine the flow unit based on well log interpretation and petrophysical properties. The flow units were identified using the Quality Index of Rock and the Indicator of Flow Zone. The Winland correlation was used to determine the pore throat size. The Lucia classification was based on fabric rock number, and cluster analysis detects rock types using well log data within the Mishrif Formation. Four rock types have been specified by the combination of these approaches grainstone-packstone, packstone-wackestone, Wackestone-Mudstone and Mudstone.

Reservoir characterization and multiphase flow property in the upper Minnelusa sandstone: Implication for geological carbon storage

This report summarizes the reservoir characterization [Wang et al., J. Hydrol., 2022] and multiphase flow property [Kou et al., J. Hydrol., 2021] in a target deep saline aquifer, upper Minnelusa sandstone in Wyoming. Multiscale petrophysical characterization and flow unit classification were carried out to identify two different facies groups: cross-bedded and massive sandstone. Based on the rock typing results, two representative core samples were selected accordingly to conduct coreflooding experiments. Results illustrate that the sub-core scale heterogeneity significantly affects CO2 -brine multiphase flow properties. As a result, the subcore scale heterogeneity should be considered during CO2 injection to reduce the uncertainties in storage and fluid flow. Cited as: Kou, Z., Wang, H., Alvarado, V. Reservoir characterization and multiphase flow property in the upper Minnelusa sandstone: Implication for geological carbon storage. Advances in Geo-Energy Research, 2022, 6(6): 535-536 . https://doi.org/10.46690/ager.2022.06.10

Multiscale petrophysical characterization and flow unit classification of the Minnelusa eolian sandstones

Integration of petrophysical and geological information is critical to simulation of subsurface carbon storage (GCS). In this sense, two depositional facies were identified from the core description and well-log interpretation, namely massive (MS) and cross-bedded (CB) facies groups. Additionally, pore-scale characteristics were studied by a combination of techniques, e.g. Nuclear Magnetic Resonance (NMR) and mercury intrusion capillary pressure (MICP). Scanning electron microscope (SEM) and petrographic analyses show that the pore structure is dominantly controlled by the depositional environment and dolomite cementation. NMR-T2 distributions of MS and CB facies show triple and quadruple modes, respectively. In addition, MICP of high- and low-permeability MS facies samples, and their CB facies group mixtures were collected. The MS sample pore-throat size distribution is uni-modal, while the triple-modal characteristic of the mixtures indicates heterogeneous pore structures at the sub-core scale for CB facies. The reliably estimates of porosity and permeability for both facies groups via NMR techniques and the MLR (Multiple Linear Regression) approach demonstrate the applicability of these techniques to eolian sandstone. Moreover, irreducible water saturation via the T2-cutoff method correlates strongly with T2LM instead of porosity. Finally, the rock quality index and flow zone indicator were calculated based on Combinable Magnetic Resonance (CMR) log interpretations. This provides direct connection to properties measured in the well. Four flow units were classified for both facies groups. Results show that better reservoir quality with significant heterogeneities is observed in the CB facies. This study highlights the importance integrating multiscale petrophysical properties including facies, pore architecture and diagenesis analysis with core- to log-scale property characterization. The results herein validate our reservoir characterization and flow unit classification in eolian reservoirs.

Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore

Rock types, pore structures and permeability are essential petrophysical outputs, and they contribute considerably to the highest degree of uncertainty in reservoir characterisation. These factors have a strong influence on exploration and field development decisions. Core analysis is the best approach for estimating permeability, assigning rock types and characterising pore networks. Wireline logs are the most often employed method for estimating the parameters at each data point of reservoirs since there are more un-cored wells than cored wells. Artificial intelligence, on the other hand, is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface data. This is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous basins. Artificial Neural Networks and Self-Organizing Maps are examples of machine learning approaches that can be used in both supervised and unsupervised modes for modelling and prediction. Eocene carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore structures. This paper outlines a novel method of rock fabric classification, pore structure characterization, flow unit classification and robust reservoir permeability modelling based on an integrated approach that incorporates core measurements, log data and machine learning techniques. The pore structure has been characterised by the combination of conventional core, capillary pressure and nuclear magnetic resonance data. Artificial neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units.

Hydraulic flow unit classification from core data: case study of the Z gas reservoir, Poland

Permeability and porosity are essential parameters for estimating hydrocarbon production from reservoir rocks. They are combined in an additional factor, the Flow Zone Index (FZI), which is the basis for defining the hydraulic flow unit (HFU). Each HFU is a homogeneous section of a reservoir rock with stable parameters that allow for media flow. Hydraulic flow units are determined from the porosity and permeability of core or well logs. The simple statistical methods are applied for HFU classification and then improve permeability prediction. This paper also shows how to quickly apply the global hydraulic elements (GHE) method for HFU classification. The methodology is tested on the Miocene formation of a deltaic facies from the Carpathian Foredeep in South-Eastern Poland.

Characterization of heterogeneous petroleum reservoir of Indian Sub-continent: An integrated approach of hydraulic flow unit – Mercury intrusion capillary pressure – Fractal model

Reservoir characterization is of key importance in upstream industry as it ensures maximum productivity of crude along with the selection of an appropriate method for economic recovery from the sub-surface reservoir. Proper identification of hydraulic flow units and a detailed understanding of pore characteristics are essential in reservoir characterization studies. Here, hydraulic flow unit concept is applied for in-depth characterization of a complex petroleum reservoir from Indian sub-continent having fine-grained sandstone, siltstone together with shale. A semi-logarithmic porosity – permeability plot exhibited large heterogeneity of the reservoir under study. An improved relationship between porosity – permeability was developed through classification of the reservoir formation under study into three hydraulic flow units based on unique flow zone indicator value. Pore size classification of studied core samples were investigated using mercury intrusion capillary pressure curves and later, validated using fractal dimensions calculated using capillary pressure fractal model. Finally, factors affecting the heterogeneity in permeability of subjected reservoir were analyzed. The study indicates that quantitative evaluation of pore system by mercury intrusion porosimetry along with the fractal dimensional analysis can efficiently be applied for verification of hydraulic flow unit classification and hence, for the detailed characterization of heterogeneous petroleum reservoir. • Thorough characterization of a crude reservoir from Indian Sub-continent is reported. • A combined approach integrating hydraulic flow unit, mercury intrusion porosimetry and fractal theory is utilized. • Hydraulic flow unit approach is beneficial for classification of heterogeneous crude reservoirs. • Pore size classification into macro, meso and micropores provide better insight to flowability in reservoir matrix.

Decameter-Scale Flow-Unit Classification in Brazilian Presalt Carbonates

Summary Rock typing into flow units (FUs) plays a pivotal role in constructing static and dynamic models of petroleum reservoirs. Decisions made by asset teams mostly depend on predictions of how fluids will percolate through the subsurface during the reservoir life cycle. In carbonate settings, dealing with rock typing is complex and can generate a large quantity of units because of diagenetic processes such as dissolution, cementation, and silicification. Most rock-typing methods in carbonates successfully classify small-scale flow heterogeneity in well resolution but fail when interpolating those facies further away from the 1D domain, because of the lack of correlation between FUs and spatial data. Seismic data can be used to detect large-scale FUs and assist the interpolation of small-scale FUs in 3D reservoir volume, thus producing more-realistic static and dynamic models. We propose a modification of the classical rock-typing methods that use permeability (k) vs. porosity (ϕ) plots and electrical properties, with a data set from the Mero Field, part of the giant Libra Field of presalt carbonate reservoirs in offshore Brazil. From the permeability cumulative S-curve analysis, we established major large-scale FUs that maintain part of the carbonate flow heterogeneity and correlate them with the elastic attributes: P-impedance (PI) and S-impedance (SI). In addition, we established a priori PI and SI correlations with the formation-factor (FF) (F) parameter to categorize large-scale FUs using the F vs. k methodology. With the large-scale FUs mapped in seismic data sets, core-plug-scale FUs can be populated into the 3D static and dynamic models using geostatistics tools, thus creating more-realistic reservoir models and assisting asset teams in the decision-making process.

Improved reservoir characterization by employing hydraulic flow unit classification in one of Iranian carbonate reservoirs

Capillary pressure curves is of great importance in reservoir characterization. Due to the reservoir heterogeneity, no single capillary pressure curve can be used for the entire reservoir depth. This paper aims to examine the application of different techniques of hydraulic flow unit (HFU) classifications in overcoming the extreme heterogeneity of the reservoir in order to improve normalization of capillary pressure in one of the Iranian carbonate oil reservoirs. In this study, well log, routine and specific core analysis lab data have been used to identify flow units by employing different techniques in a heavy oil carbonate reservoir in the south of Iran. These techniques include gamma ray and density log method, flow zone indicator, capillary pressure curves and Winland parameter. Then, mercury injection data has been used by employing Desouky's method to normalize capillary pressure curves for each identified flow unit. According to this study HFU classification significantly improved normalizing of capillary pressure curves. Cited as : Rafiei, Y., Motie, M. Improved reservoir characterization by employing hydraulic flow unit classifification in one of Iranian carbonate reservoirs. Advances in Geo-Energy Research, 2019, 3(3): 277-286, doi: 10.26804/ager.2019.03.06

Flow units and rock type for reservoir characterization in carbonate reservoir: case study, south of Iraq

The current work is focused on the rock typing and flow unit classification for reservoir characterization in carbonate reservoir, a Yamama Reservoir in south of Iraq (Ratawi Field) has been selected, and the study is depending on the logs and cores data from five wells which penetrate Yamama formation. Yamama Reservoir was divided into twenty flow units and rock types, depending on the Microfacies and Electrofacies Character, the well logs pattern, Porosity–Water saturation relationship, flow zone indicator (FZI) method, capillary pressure analysis, and Porosity–Permeability relationship (R35) and cluster analysis method. Four rock types and groups have been identified in the Yamama formation depending on the FZI method, where the first group represents the bad reservoir quality (FZI-1) (Mudstone Microfacies and Foraminiferal wackestone Microfacies), the second group reflects a moderate quality of reservoir (FZI-2) (Algal wackestone–Packstone Microfacies and Bioclastic wackestone–Packstone Microfacies), the third group represents good reservoir quality (FZI-3) (Peloidal Packstone–Grainstone Microfacies), and the fourth group represents a very good reservoir quality (FZI-4) (Peloidal–oolitic Grainstone Microfacies). Capillary pressure curves and cluster analysis methods show four different rock types: a very good quality of reservoir and porous (Mega port type) (FZI-4) (Peloidal–oolitic Grainstone Microfacies) with a low irreducible Water saturation (Swi), good quality of reservoir and porous (Macro port type) (FZI-3) (Peloidal Packstone–Grainstone Microfacies), moderate quality of reservoir (Meso port type) (FZI-2) (Algal wackestone–Packstone Microfacies and Bioclastic wackestone–Packstone Microfacies), and a very fine-grained with bad reservoir quality (Micro port type) (FZI-1) (Mudstone Microfacies and Foraminiferal wackestone Microfacies) and with the higher displacement of pressure). These capillary pressure curves support the subdivision of the main reservoir unit to flow units.

Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field, Jiyang depression

KNN (K-Nearest Neighbors) clustering in machine learning is a very efficient clustering method applied in lithology identification, reservoir type recognition, flow unit classification and so on, but it is not working consistently because of its inherent limitation, like its initial center selection and clustering center shift caused by outliers. Moreover, KNN clustering is based on a mathematical algorithm, and does not necessarily cluster the space due to the geological and petrophysical aspects. To address this issue, an optimized KNN clustering method based on weighed cosine distance is proposed to better fit the lithology model. To deal with the problem of the initial center selection and outliers, a set of statistic methods is conducted. A set of data, sampled by different logging series, is made use to feature information about lithology as much as possible. New distance algorithm selection based on geology model and well logging data similarity attribute is obtained when utilizing. Taking the Mesozoic strata in Gaoqing field as example, a procedure fitter for utilization of machine learning technique in lithology identification is carried on, and the overall accurate rate of the optimized K-means clustering in lithology identification raised nearly 10% compared with the traditional K-means clustering especially in the differentiation between fine-sand and coarse-grained clastic sand. The predicted lithology match the core plugs better than the traditional KNN clustering.

Identification of flow units using the joint of WT and LSSVM based on FZI in a heterogeneous carbonate reservoir

The objective of this study was to develop an accurate method for predicting hydraulic unit types in a heterogeneous carbonate reservoir. There is a significant practical potential in the use of the flow unit characterization. Identification of flow units in inhomogeneous carbonate reservoir presents a great challenge to geologists and engineers. A new method for dividing the flow units was proposed in this study based on the joint of wavelet transform (WT) and least squares support vector machine (LSSVM) within the most productive carbonate reservoir of the Minghuazhen Formation in Region A, Block X in the Petrochina Dagang oilfield. Petrophysical properties derived from core data and logging from 21 representative wells were analyzed. The flow units were classified as five types based on the flow zone index (FZI) approach. The WT and LSSVM were jointly used for learning and training each unit. The well logs were broken down into high and low frequency data using WT. Sensitivity analysis of parameters of training samples to select the largest impact was performed with C5.0 decision tree to obtain a WT-trained set. A predictive model was then established by training LSSVM model. The final trained model with the identification rule and criterion for the classification of flow units was used for identifying flow units in the cored and non-cored intervals of reservoir. The result from this study is consistent with core data and is more accurate than that from the previous investigations. It is concluded that using the combination of the WT and LSSVM improved the accuracy of classification of flow units in the Minghuazhen Formation.

Application of integrated rock typing and flow units identification methods for an Iranian carbonate reservoir

During the exploration phase of any oil and gas field, recognition of borehole type, its completion approach, and understanding of the future reservoir performance are directly interrelated to identification of the reservoir's rock types and flow units. Identifying different geological events, lithofacies characteristics, diagenetic processes, and level of heterogeneity can be the first step for knowing rock types and consequently flow units in its reservoir layers. These result in an acceptable dynamic model of a field development and relatively reliable production forecasts.Focus of this paper is on rock typing and flow unit classification for a carbonate reservoir of a green field. Four petrophysical methods including Rock Fabric Number (RFN), Winland porosity-permeability plot (R35), Reservoir Quality Index/Flow Zone Indicator (RQI/FZI), and Bulk Volume Water (BVW) were implemented on four exploration wells. At the next step graphical tools (Stratigraphic Modified Lorenz Plot (SMLP), Modified Lorenz Plot (MLP) and Stratigraphic Flow Profile (SFP) were used to locate established flow units and rock types within the reservoir column. Lastly, variation and frequency of defined flow units have been used to achieve a satisfactory understanding of the level of heterogeneity and the possible effect on well production.Integration of defined flow units by different methods inferred a high level of heterogeneity in the studied reservoir and the BVW was chosen as the best method of rock typing for feeding to the static model. The BVW method assists in considering main variation of reservoir properties meanwhile avoid complexity of models. During modelling, choosing an optimum number of rock types for fine-grid model with small dimensions cells (50 m*50 m*1 m) can help to separate different layers vertically without necessity of dividing them to many distinct subzones. Usually, groping numerous subzones during up scaling is confusing and complicate; likewise, in a coarse grid may omit or mix some frequency of thin seal and productive subintervals in reservoir zones. Comparing achieved flow unit classification with outcomes of production test showed in this reservoir with high level of heterogeneity an adequate type of well is vertical and highly deviated ones, moreover, dual completion can be the best type of production which separate high and low productive zones.

Classification and features of single-well flow units in a carbonate reservoir - Taking the NT oil field at the eastern edge of Pre-Caspian Basin as an example

Identifying the carbonate reservoirs has always been a challenge to geological exploration, while reasonable classification of flow units is the bottleneck in this exploitation. While taking the NT oil field at the eastern edge of Pre-Caspian Basin as an example, this paper proposes the classification of flow units into five categories based on previous flow-unit classification theory and actual oilfield features by using the pore throat radius at the mercury injection saturation of 35% as the main judging criterion. In this paper, the features of various flow units have also been analyzed. The type-I flow units, mainly found in dolomite and algal reef limestone reservoirs, have the highest production capacity. Given the existence of corrosion and dolomitization, they are mainly fracture-cave composite reservoirs or fracture pore reservoirs. As far as the type-I flow units are concerned, the flow index is > 1.42 for KT-I stratum and > 1.55 for KT-II stratum. The production capacity and reservoir quality of type-II-IV flow units would decline in turn. The type-V flow units are argillaceous limestone, with a very low production capacity and a flow index being 0.01-0.05 for KT-I and 0.03-0.05 for KT-II.

A study on remaining oil distribution in a carbonate oil reservoir based on reservoir flow units

Abstract Reservoir Γ north, which belongs to Zhanzhol, Kazakhstan, a fracture-pore typed carbonate oilfield, has a variety of reservoir types. According to the combination pattern of different voids and the relationship between porosity and permeability, carbonate reservoirs are classified into four types which are fracture-cavity-pore typed (referred to as composite typed here after), fracture-pore typed, pore typed and fracture typed, and the identification of which by well logging data is realized. There is a little difference in the producing degree within the same reservoir type for composite typed and fracture typed reservoirs. However, there are large differences in the producing degree for fracture-pore typed and pore typed reservoirs. In order to appraise the producing degree of reservoirs more exactly, key parameters affecting the producing degree of fracture-pore typed and pore typed reservoirs are selected separately, which, for the fracture-pore typed are reservoir quality index ( RQI ), pore throat radius at mercury saturation of 50% ( R 50 ), total permeability ( K f + K m ), and the ratio of fracture permeability to matrix permeability ( K f / K m ), and for the pore typed are RQI, R 50 , and matrix permeability ( K m ). With the integrated consideration of matrix and fracture, a flow unit classification method for dual medium reservoir is established, and taking these parameters as clustering variants, four types of reservoirs are further divided into six kinds of reservoir flow units by the means of neural network clustering technology. Based on reservoir flow unit modeling, a numerical simulation model is built with each type of reservoir flow units adopting its corresponding relative permeability curve, based on which, the distribution regularity of remaining oil of reservoir Γ north is studied and demonstrated. Eight new wells are designed according to the research result, and put into production in 2013 with the initial daily oil production rate being 2.3 times that of nearby old wells.

Application of Colligation Coefficient Method Classifying Flow Units

The classification of flow units is the deep study of reservoir heterogeneity. In this paper we use a part of some oilfield as an example. Vertical flow units subdivision is up to the standard of single sand layer, which is the delicate reservoir anatomy. We use the related parameters and core data to obtain the quantitative standard of horizontal flow unit classification of no-coring well by the colligation coefficient method. Flow units distribution is consistent with distribution of sedimentary facies, it changes only in partial zone. Flow units distribution basically reflects the variation of sedimentary facies, it shows the plane macroscopic heterogeneity of reservoir.

Research on Reservoir Flow Unit Classification at Block D404 of Xinzhan Oilfield in Songliao Basin

Remaining oil is enrichment relative, and the reservoir heterogeneity is serious at D404 block of Xinzhan oilfield, Songliao Basin. It is necessary of classification research to guide the infilling and the injection-production system adjustment. Through consolidation analysis of Core sample, porosity, permeability, shale content and flow zone index are selected as classification parameters of the division of flow unit. Using flow zone indicator method , the reservoir was divided into four types including I,II, III,IV types. The Classification results show that I type is the best reservoir and the strongest percolation capacity, and II,III types are the second, however IV type reservoir is the worst, the weakest seepage ability. the flow unit classification has solved some problem that the heterogeneity of reservoir has an influence on logging response, laying the foundation for the watered-out mechanism research and for the establishment of the identification standard.