Showing papers on "Reservoir modeling published in 2012"

Enhanced Oil Recovery

Abstract: Oil exploration and production actively began 100 years ago, from the present day. What we see now, known as primary production by natural flowing of oil by pumped wells. This covers 15% of oil recovery from reservoir. Later 25% of oil is recovered by water flooding is activated which is termed as secondary production. There is still significant oil left in the reservoir, if there is no proper employment of commercial valuable techniques for the production, the oilwell would simply be abandoned. But as the demand for oil kept raising new techniques emerged, eventually from those EOR was a successful in artificial up lifting of oil from the reservoir by providing enough pressure to the trapped oil to flow out of well. It is a tertiary production. EOR is different because it works from microscopic levels as well as by injecting the substances like gases, special liquid (polymers) and stream in the form of injection through injecting wells for oil recovery, which is termed as Enhanced Oil Recovery (EOR). The flooding in EOR, categorized as chemical flooding (by chemicals), stream or thermal injection (by stream) and miscible gas drive (CO2, N2 and LPG). These flooding will alter the physical and chemical properties of reservoirs for the flow of oil out of the well. EOR can unlock a percentage of 30-45% of trapped oil. After naming as successful technique in onshore for tertiary production, research is still going to implement EOR in offshore, to improve tertiary production and exploit hidden billion barrels of oil.

Multiple-point geostatistical modeling based on the cross-correlation functions

Abstract: An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel- and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory- and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements.

Hierarchical Bayesian lithology/fluid prediction: A North Sea case study

Abstract: Seismic 3D amplitude variation with offset (AVO) data from the Alvheim field in the North Sea are inverted into lithology/fluid classes, elastic properties, and porosity. Lithology/fluid maps over hydrocarbon prospects provide more reliable estimates of gas/oil volumes and improve the decision concerning further reservoir assessments. The Alvheim field is of turbidite origin with complex sand-lobe geometry and appears without clear fluid contacts across the field. The inversion is phrased in a Bayesian setting. The likelihood model contains a convolutional, linearized seismic model and a rock-physics model that capture vertical trends due to increased sand compaction and possible cementation. The likelihood model contains several global model parameters that are considered to be stochastic to adapt the model to the field under study and to include model uncertainty in the uncertainty assessments. The prior model on the lithology/fluid classes is a Markov random field that captures local vertical/horizonta...

Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method

Abstract: We presented a new methodology for seismic reservoir characterization that combined advanced geostatistical methods with traditional geophysical models to provide fine-scale reservoir models of facies and reservoir properties, such as porosity and net-to-gross. The methodology we proposed was a stochastic inversion where we simultaneously obtained earth models of facies, rock properties, and elastic attributes. It is based on an iterative process where we generated a set of models of reservoir properties by using sequential simulations, calculated the corresponding elastic attributes through rock-physics relations, computed synthetic seismograms and, finally, compared these synthetic results with the real seismic amplitudes. The optimization is a stochastic technique, the probability perturbation method, that perturbs the probability distribution of the initial realization and allows obtaining a facies model consistent with all available data through a relatively small number of iterations. The pr...

Advances in carbonate exploration and reservoir analysis

Abstract: Carbonate reservoirs contain an increasingly important percentage of the world's hydrocarbon reserves. This volume presents key recent advances in carbonate exploration and reservoir analysis. As well as a comprehensive overview of the trends in carbonate over the years, the volume focuses on four key areas: 1. emerging plays and techniques – with special reference to lacustrine plays in syn-rift basins and development of super-giant heavy oil plays 2. improved reservoir characterization – with examples from the Middle East and Europe and case studies of how outcrop analogues can provide key data for input to geological models 3. impact of fractures and faults in carbonates –contributors highlight the need for integrated structural and diagenetic approaches in order to understand how fractures evolve as fluid-flow conduits 4. advances in geomodelling of carbonate reservoirs –several papers discuss the application of new and innovative geomodelling and geostatistical techniques to carbonate reservoirs.

Integrated reservoir characterization and modeling in support of enhanced oil recovery for Bakken

Abstract: .................................................................................................................................. iii TABLE OF CONTENTS .............................................................................................................. v LIST OF FIGURES ...................................................................................................................... ix LIST OF TABLES ...................................................................................................................... xvi ACKNOWLEDGEMENT ........................................................................................................ xviii CHAPTER

Fracture density estimation from petrophysical log data using the adaptive neuro-fuzzy inference system

Abstract: Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.

Joint inversion approaches for geophysical electromagnetic and elastic full-waveform data

Abstract: We present joint inversion approaches for integrating controlled source electromagnetic data and seismic full-waveform data for geophysical applications. The first approach is the joint petrophysical inversion carried out by reconstructing petrophysical parameters such as porosity and saturations instead of the usual geophysical parameters such as resistivity, seismic velocities and mass density. This approach utilizes the strong correlation between the electromagnetic and seismic parameters through the petrophysical relationships. Another approach that does not require the a priori petrophysical correlation is the joint structural inversion method. In this approach, the inversion is carried out by employing a regularization function for enforcing the structural similarity between the resistivity and the seismic velocities and the mass density. In this method, we employ the cross-gradient function, which has been shown on many occasions to be quite effective. By using a time-lapse reservoir monitoring example, we show that both joint inversion approaches produce results that are superior to those obtained by disjointed inversions. Hence, these joint inversions have great potential to be the next-generation tools for reservoir characterization and monitoring.

Advances in Measurement Standards and Flow Properties Measurements for Tight Rocks such as Shales

Abstract: Determination of permeability of unconventional reservoirs is critical for reservoir characterization, forecasting production, determination of well spacing, designing hydraulic fracture treatments, and a number of other applications. In many unconventional reservoirs, gas is produced from tight rocks such as shale. Currently the most commonly used industry method for measuring permeability is the Gas Research Institute (GRI) technique, or its variants, which involve the use of crushed samples. The accuracy of such techniques, however, is questionable because of a number of inadequacies such as the absence of reservoir overburden stress while conducting these measurements. In addition to questionable accuracy of crushed rock techniques, prior studies have indicated that there is significant variability in results reported by different laboratories that utilize crushed-rock technique to measure permeability on shale samples. Alternate methods are required to obtain accurate and consistent data for tight rocks such as shales. In this paper we discuss a robust steady-state technique for measuring permeability on intact tight rock samples under reservoir overburden stress. Permeability measurement standards for low permeability samples are critical for obtaining consistent results from different laboratories making such measurements, regardless of the method used for measuring permeability. In this paper we present permeability measurement standards developed based on first principles that serve as the “ground-truth” for permeability in the 10 – 10,000 nanoDarcy range. These standards can be used to calibrate any permeability measurement apparatus used to measure permeability on intact tight rock samples such as shales, to enable delivery of consistent results across different laboratories conducting measurements on intact tight rock samples.

Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis

Abstract: Formation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The ...

Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir

Abstract: Permeability prediction has been a challenge to reservoir engineers due to the lack of tools that measure it directly. The most reliable data of permeability obtained from laboratory measurements on cores do not provide a continuous profile along the depth of the formation. Recently, researchers utilized statistical regression, neural networks, and fuzzy logic to estimate both permeability and porosity from well logs. Unfortunately, due to both uncertainty and imprecision, the developed predictive modelings are less accurate compared to laboratory experimental core data. This paper presents functional networks as a novel approach to forecast permeability using well logs in a carbonate reservoir. The new intelligence paradigm helps to overcome the most common limitations of the existing modeling techniques in statistics, data mining, machine learning, and artificial intelligence communities. To demonstrate the usefulness of the functional networks modeling strategy, we briefly describe its learning algorithm through simple distinct examples. Comparative studies were carried out using real-life industry wireline logs to compare the performance of the new framework with the most popular modeling schemes, such as linear/nonlinear regression, neural networks, and fuzzy logic inference systems. The results show that the performance of functional networks (separable and generalized associativity) architecture with polynomial basis is accurate, reliable, and outperforms most of the existing predictive data mining modeling approaches. Future work can be achieved using different structure of functional networks with different basis, interaction terms, ensemble and hybrid strategies, different clustering, and outlier identification techniques within different oil and gas challenge problems, namely, 3D passive seismic, identification of lithofacies types, history matching, rock mechanics, viscosity, risk assessment, and reservoir characterization.

Rock physics template (RPT) analysis of well logs and seismic data for lithology and fluid classification in Cambay Basin

Abstract: The Cambay Basin is 450-km-long north–south-trending graben with an average width of 50 km, having maximum depth of about 7 km. The origin of the Cambay and other Basins on the western margin of India are related to the break up of the Gondwana super-continent in the Late-Triassic to Early-Jurassic (215 ma). The structural disposition of the Pre-Cambrian basement—a complex of igneous and metamorphic rocks exposed in the vicinity of the Cambay Basin—controls its architecture. The principal lineaments in the Basin are aligned towards NE-SE, ENE-WSW and NNW-SSE, respectively. Rock physics templates (RPTs) are charts and graphs generated by using rock physics models, constrained by local geology, that serve as tools for lithology and fluid differentiation. RPT can act as a powerful tool in validating hydrocarbon anomalies in undrilled areas and assist in seismic interpretation and prospect evaluation. However, the success of RPT analysis depends on the availability of the local geological information and the use of the proper model. RPT analysis has been performed on well logs and seismic data of a particular study area in mid Cambay Basin. Rock physics diagnostic approach is adopted in the study area placed at mid Cambay Basin to estimate the volume in the reservoir sands from 6 wells (namely; A, B, C, D, E and F) where oil was already encountered in one well, D. In the study area, hydrocarbon prospective zone has been marked through compressional (P wave) and shear wave (S wave) impedance only. In the RPT analysis, we have plotted different kinds of graphical responses of Lame’s parameters, which are the function of P-wave velocity, S-wave velocity and density. The discrete thin sand reservoirs have been delineated through the RPT analysis. The reservoir pay sand thickness map of the study area has also been derived from RPT analysis and fluid characterization. Through this fluid characterization, oil-bearing thin sand layers have been found in well E including well D. The sand distribution results prove that this methodology has able to perform reservoir characterization and seismic data interpretation more quantitatively and efficiently.

Impact of Diagenetic Alterations on the Petrophysical and Multiphase Flow Properties of Carbonate Rocks Using a Reactive Pore Network Modeling Approach

Abstract: Sedimentary reservoir rocks generally have complex and heterogeneous pore networks that are related to the original depositional rock texture and subsequent diagenetic alterations. Such alterations are in part controlled by the original mineralogy and sedimentological facies, the compaction history, the involved fluids (and rock/fluid interactions), the flow history and the related physico-chemical conditions. During the diagenetic evolution (paragenesis), cycles of alternating dissolution (porosity enhancement) and precipitation (porosity destruction) caused by changes in chemical and thermodynamic conditions may lead to heterogeneous rock structure at both local and reservoir scale.In the absence of cored plugs to measure the petrophysical properties (i.e. porosity, permeability and formation factor) and multiphase flow properties (i.e. capillary pressure, relative permeability and resistivity index), a numerical tool that calculates these properties from pore structure data by predicting its evolution during the diagenetic cycle is of great interest for the petroleum industry and reservoir characterization studies.A Pore Network Model (PNM) provides opportunities to study transport phenomena in fundamental ways because detailed information is available at the pore scale. It has been used over the last decades to understand basic phenomena such as capillarity, multiphase flow or coupled phenomena. In particular, this modeling approach is appropriate to study the rock/fluid interactions since the mass exchange at surfaces can be modeled explicitly. It can provide quantitative information both on the effective transport property modifications due to the reactions and on the structure evolution resulting from dissolution/precipitation mechanisms. In the present paper, this approach is used to study the effect of the diagenetic cycle on the petrophysical properties of carbonate rocks. It involves three discrete steps. The first step consists of replacing the original complex pore structure of real porous media by a conceptual network. The second step consists of resolving the governing equations of the precipitation and dissolution phenomena (i.e. reactive convection diffusion equation) in the conceptual 3D pore network and deducing the local reactive fluxes and the motion of the fluid-solid interface. The third step consists of updating the new pore structure and calculating the new petrophysical properties of the modified porous media. Those steps are repeated in order to mimic a given diagenetic scenario. Finally, the multiphase flow properties of the current porous media are calculated.The impact of one diagenetic cycle of dissolution and precipitation on the pore networks’ heterogeneity and consequently on the petrophysical properties (i.e. porosity and permeability) and multiphase flow properties (i.e. relative permeability and capillary pressure) have been investigated. The permeability and porosity evolution during a given diagenetic cycle are calculated and analyzed as a function of the relevant dimensionless numbers (Peclet and Damkohler numbers) that characterize the flow and reaction regime. The correlation between these numbers and the dissolved/precipitated layer thickness distribution is investigated.This work contributes to improve the understanding of the impact of dissolution and precipitation on permeability and porosity modification. Using the PNM approach, multiphase flow properties and permeability-porosity relationship have been determined for different reactive flow regimes. These relationships are relevant input data to improve the quality of reservoir simulation predictions.

Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

Abstract: A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pr...

Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers

Abstract: History matching provides to reservoir engineers an improved spatial distribution of physical properties to be used in forecasting the reservoir response for field management. The ill-posed character of the history-matching problem yields nonuniqueness and numerical instabilities that increase with the reservoir complexity. These features might cause local optimization methods to provide unpredictable results not being able to discriminate among the multiple models that fit the observed data (production history). Also, the high dimensionality of the inverse problem impedes estimation of uncertainties using classical Markov-chain Monte Carlo methods. We attenuated the ill-conditioned character of this history-matching inverse problem by reducing the model complexity using a spatial principal component basis and by combining as observables flow production measurements and time-lapse seismic crosswell tomographic images. Additionally the inverse problem was solved in a stochastic framework. For this purpose,...

Quantification and Prediction of the 3D Pore Network Evolution in Carbonate Reservoir Rocks

Abstract: This study presents an integrated approach that allows the reconstruction and prediction of 3D pore structure modifications and porosity/permeability development throughout carbonate diagenesis. Reactive Pore Network Models (PNM-R) can predict changes in the transport properties of porous media, resulting from dissolution/cementation phenomena. The validity and predictability of these models however depend on the representativeness of the equivalent pore networks used and on the equations and parameters used to model the diagenetic events. The developed approach is applied to a real case of a dolostone rock of the Middle East Arab Formation. Standard 2D microscopy shows that the main process affecting the reservoir quality is dolomitisation, followed by porosity enhancement due to dolomite dissolution and secondary porosity destruction by cementation of late diagenetic anhydrite. X-ray μ -CT allows quantifying the 3D volume and distribution of the different sample constituents. Results are compared with lab measurements. Equivalent pore networks before dolomite dissolution and prior to late anhydrite precipitation are reconstructed and used to simulate the porosity, permeability characteristics at these diagenetic steps. Using these 3D pore structures, PNM-R can trace plausible porosity-permeability evolution paths between these steps. The flow conditions and reaction rates obtained by geochemical reaction path modeling can be used as reference to define PNM-R model parameters. The approach can be used in dynamic rock typing and the upscaling of petrophysical properties, necessary for reservoir modeling.

Reservoir characterization based on seismic spectral variations

Abstract: The seismic frequency spectrum provides a useful source of information for reservoir characterization. For a seismic profile presented in the time-space domain, a vector of the frequency spectrum can be generated at every sampling point. Because the spectrum vectors at different time-space locations have different variation features, I attempt for the first time to exploit the variation pattern of the frequency spectrum for reservoir characterization, and test this innovative technology in prediction of coalbed methane (CBM) reservoirs. The prediction process implicitly takes account of the CBM reservoir factors (such as viscosity, elasticity, cleat system, wave interference within a coal seam, etc.) that affect the frequency spectrum, but strong amplitudes in seismic reflections do not necessarily show any influence in clustering analysis of spectral variation patterns. By calibrating these variation patterns quantitatively with CBM productions in well locations, we are able to characterize the s...

Seismic low-frequency-based calculation of reservoir fluid mobility and its applications

Abstract: Low frequency content of seismic signals contains information related to the reservoir fluid mobility. Based on the asymptotic analysis theory of frequency-dependent reflectivity from a fluid-saturated poroelastic medium, we derive the computational implementation of reservoir fluid mobility and present the determination of optimal frequency in the implementation. We then calculate the reservoir fluid mobility using the optimal frequency instantaneous spectra at the low-frequency end of the seismic spectrum. The methodology is applied to synthetic seismic data from a permeable gas-bearing reservoir model and real land and marine seismic data. The results demonstrate that the fluid mobility shows excellent quality in imaging the gas reservoirs. It is feasible to detect the location and spatial distribution of gas reservoirs and reduce the non-uniqueness and uncertainty in fluid identification.

Porosity and Permeability Estimation using Neural Network Approach from Well Log Data

Abstract: Summary In recent years, artificial intelligence techniques, and neural networks in particular, have gained popularity in solving complex nonlinear problems. Permeability, porosity and fluid saturation are three fundamental characteristics of reservoir systems that are typically distributed in a spatially non-uniform and non-linear manner. In this context, porosity and permeability prediction from well log data is well-suited to neural networks and other computer based techniques. The present study aims to estimate formation porosity and permeability from digital well log data using an artificial neural network (ANN) approach. A representative case study from the Alberta Deep Basin is presented. Five well log responses (Gamma Ray Log (GR), Deep Resistivity (RD), Formation Density (DEN), Neutron Porosity (PHIN) and Density Porosity (PHID)) are used as inputs in the ANN to predict porosity and permeability. Core porosity and permeability are used as target data in the ANN to test the prediction. The accuracy of the ANN approach is tested by regression plots of predicted values of porosity and permeability with core porosity and permeability respectively. Excellent matching of core data and predicted values reflects the accuracy of the technique. ANN is a fast and accurate method for the prediction of reservoir properties and could be applied in reservoir modeling and characterization.

History Matching of Production and 4D Seismic Data: Application to the Girassol Field, Offshore Angola

Abstract: Time-lapse seismic provides a source of valuable information about the evolution in space and time of the distribution of hydrocarbons inside reservoirs. Seismic monitoring improves our understanding of production mechanisms and makes it possible to optimize the recovery of hydrocarbons. Although 4D seismic data are increasingly used by oil companies, they are often qualitative, due to the lack of suitable interpretation techniques. Recent modeling experiments have shown that the integration of 4D seismic data for updating reservoir flow models is feasible. However, methodologies based on sequential interpretation of 4D seismic data, trial and error processes and fluid flow simulation tests require a great effort from integrated teams. The development of assisted history matching techniques is a significant improvement towards a quantitative use of 4D seismic data in reservoir modeling. This paper proposes an innovative methodology based on advanced history matching solutions to constrain 3D stochastic reservoir models to both production history and 4D seismic attributes. In this approach, geostatistical modeling, upscaling, fluid flow simulation, downscaling and petro-elastic modeling are integrated into the same history matching workflow. Simulated production history and 4D seismic attributes are compared to real data using an objective function, which is minimized with a new optimization algorithm based on response surface fitting. The gradual deformation method is used to constrain the facies realization, globally or locally, which populates the geological model at the fine scale. Moreover, a new method is proposed to update facies proportions during the optimization process according to 4D monitoring information. We present here a successful application to the Girassol field. Girassol is a large, complex and faulted turbidite field, located offshore Angola. First, a detailed geostatistical geological model was built to describe reservoir heterogeneity at the fine scale, while respecting 3D base seismic data. Second, the model was constrained to production data and 4D seismic attributes, applying gradual deformation to facies realizations and varying facies proportions. The integration of 4D seismic data led to better production forecasts and improved predictions confirmed by a new seismic survey shot two years after the history matching period. 4D seismic data also contributed to better characterize the spatial distribution of heterogeneities in the field. As a result, the fine scale geological model was improved consistently with respect to the fluid flow simulation model and the observed data. The Girassol study, already presented in (Roggero et al., 2007, 2008), has been updated with recent information and a more detailed presentation concerning the construction of the geological model based on 3D seismic data.

Fractal Models in Exploration Geophysics: Applications to Hydrocarbon Reservoirs

Abstract: Researchers in the field of exploration geophysics have developed new methods for the acquisition, processing and interpretation of gravity and magnetic data, based on detailed investigations of bore wells around the globe. "Fractal Models in Exploration Geophysics" describes fractal-based models for characterizing these complex subsurface geological structures. The authors introduce the inverse problem using a fractal approach which they then develop with the implementation of a global optimization algorithm for seismic data: very fast simulated annealing (VFSA). This approach provides high-resolution inverse modeling results - particularly useful for reservoir characterization. This title serves as a valuable resource for researchers studying the application of fractals in exploration, and for practitioners directly applying field data for geo-modeling. It discusses the basic principles and practical applications of time-lapse seismic reservoir monitoring technology-application rapidly advancing topic. It provides the fundamentals for those interested in reservoir geophysics and reservoir simulation study. It demonstrates an example of reservoir simulation for enhanced oil recovery using CO2 injection.

Diagnosing Fracture Network Pattern and Flow Regime Aids Production Performance Analysis in Unconventional Oil Reservoirs

Abstract: Many tight or shale gas wells exhibit a linear flow regime that can last for years. However, production analysis in unconventional oil reservoirs, such as the Bakken, shows that the linear flow regime is not the only dominant flow regime. Field data suggest that the duration of boundary-dominated flow influenced by the stimulated-reservoir volume (SRV) and compound-linear flow generally overshadow the early-time linear flow regime. Depending on the fracture network or SRV patterns, formation linear flow in unconventional oil reservoirs may only last for a few months but contribute about 30% of the total estimated ultimate recovery (EUR). This study develops a procedure for identification of different fracture network patterns and inference of related flow parameters based on analytical methods. The reservoir description so derived is transported to a numerical reservoir-flow simulation model to capture the effects of compaction, multiphase flow behavior, and various flow regimes in an unconventional oil reservoir system. This coupled approach helps illuminate reservoir performance, which allows insights into history matching. In particular, we demonstrate (a) fracture network patterns and flow regime diagnosis through rate-transient analysis; (b) coupled numerical reservoir simulation with analytical modeling results for performance-constrained history matching; (c) sensitivity analysis on the heterogeneity effect, compaction effect, and multiphase flow effects; and (d) field application of the proposed procedure on Bakken wells. This proposed method demonstrates that analytical methods should be used before undertaking a detailed numerical reservoirflow simulation study. This understanding paves the way for much improved reservoir characterization in unconventional oil reservoirs.