Field Development Scenarios Research Articles

Optimizing reservoir characterization: insights from integrated data analysis

The Onshore Hydrocarbon prospectivity of the Niger Delta X-field is examined through the integration of 3D seismic and recorded information from well log data. Probable reservoirs of hydrocarbon-bearing were delineated to tackle the non-uniqueness in the identification of hydrocarbon quantity of concern. Three significant faulting patterns or systems were delineated and their architectural attributes depict a typical Niger Delta embedded anticlinal structural pattern. The study field exhibited both synthetic and antithetic structures, hence only fault on delineated reservoirs was used for structural modeling. All well locations were sited within the fault-supported synthetic and anticlinal structures and the static characteristics within the well coordinates were analyzed through petrophysical assessment. Depicted reservoir sands show low gamma ray readings, low volume of shale, considerate hydrocarbon saturation and low water saturation. The established petrophysical models showed a better net-to-gross (NTG) ratio, for the entire delineated reservoir units. This has contributed to the assessment of hydrocarbon-bearing reservoir units before employing the strategy for field development scenario, in order to eliminate dry hole drilling campaign. These will contribute to the reduction of operational costs, having delineated the accurate geometry and petrophysical model of hydrocarbon reservoir units.

Forecasting the efficiency of waterflooding, thermal and chemical enhanced oil recovery methods in Bobrikovian reservoirs

The development of reserves contained viscous and highly viscous oil poses one of the major challenges in modern reservoir engineering. The poor performance of conventional waterflooding prompts the application of enhanced oil recovery (EOR) methods, of which thermal and chemical EOR are considered the most efficient. Reservoir simulation is a powerful tool for evaluating EOR performance. Serial runs of a history-matched model of a Bobrikovian reservoir are used to evaluate the incremental oil production due to each EOR technique and formulate the efficiency limits of the process performance criteria. In contrast to previous studies that evaluated EOR applications using reservoir simulation modeling, an entirely new quality model is derived through automatic history matching performed by solving an inverse problem. Another novel feature is the automated selection of the optimum field development scenario given a user-defined objective function. Evaluating the performance of hot water injection in a Bobrikovian reservoir suggests that this technology yields positive effects when the injected water temperature ranges from 50 °C to 90 °C. The surfactant–polymer flooding method is also investigated. The largest incremental oil production with a constant surfactant concentration of 0.5% is observed for a polyacrylamide concentration of 0.25%, which can be attributed to a water cut reduction due to plugging the well-drained and flushed reservoir zones. The greatest incremental oil production is achieved when injecting 1.5% surfactant and 0.25% polyacrylamide. The highest efficiency per ton of injected chemicals is obtained when injecting 0.5% polyacrylamide and 0.10% surfactant. Keywords: High-viscosity oil; EOR; thermal methods; surfactant-polymer flooding; reservoir simulation; history matching; injection simulation scenario.

Multiobjective History Matching Using Machine Learning Proxies-Assisted Iterative Rejection Sampling

Summary The effectiveness of subsurface assessment for various field development scenarios relies on accurately quantifying uncertainties during production forecasts. This study proposes a new design-of-experiment (DoE)-based multiobjective history matching (HM) workflow using machine learning (ML) proxies-assisted iterative rejection sampling (IRS). The HM target is to improve the HM outcome of all the objectives simultaneously and attain a series of reliable simulation models for production forecasting after a predefined number of iterations. The IRS workflow takes full advantage of various proxies to efficiently explore the solution space via Monte Carlo (MC) sampling. The MC samples are iteratively rejected according to each objective function, and the new simulation designs for the next iteration are intentionally selected from the accepted MC samples to best preserve the subsurface uncertainty. The strengths of IRS over the other two optimizers commonly used in the oil and gas industry, genetic algorithm (GA) and particle swarm optimization (PSO), are validated through three applications—an analytic six-parameter three-objective minimization problem, a complex 120-parameter five-objective HM for Brugge field, and a 41-parameter three-objective HM study for an offshore asset in the Gulf of Mexico. By comparing the performance against GA and PSO, this study demonstrates that IRS is able to reduce the values of all the objectives rapidly and guarantees a better posterior distribution of simulation samples in the parameter space. Thus, IRS would yield a series of reliable simulation models with a close match to the observation data and preserve a high diversity of model parameters. These models can play an important role in modern reservoir management to facilitate the decision-making process. In addition, the IRS algorithm itself only costs 0.2–0.5% of the computational resource and 1.4–12.0% of the waiting time, according to the field study. To the best of our knowledge, the IRS workflow is the first one that integrates various types of ML proxies into the multiobjective HM process in the literature.

Estimation of porosity and volume of shale using artificial intelligence, case study of Kashafrud Gas Reservoir, NE Iran

The key problem in oil exploration and engineering is the lack of accurate and reliable data about the reservoir parameters of a field. Having a precise assessment of petrophysical properties can provide the ability to make decisions with a high degree of confidence about planning for production, exploitation, and further field development scenario. In this research, an artificial intelligence (AI)-based approach was developed to improve the estimation of reservoir parameters including porosity and volume of shale, which has a significant role in different stages of hydrocarbon exploration, in the Kashafrud Gas Reservoir in the northeast of Iran. For this purpose, we measured the petrophysical properties of 27 samples of the Kashafrud Formation. To increase the amount of data for employing a multilayer perceptron (MLP) artificial neural network (ANN), a geostatistical algorithm was used to increase the amount of laboratory measured data of porosity and volume of shale to 686 and 702, respectively. In addition, 2263 well-logging data from the same well were provided. The optimal MLP network with the topology of 6-7-1, and 6-8-1 was selected to estimate the porosity and shale volume with mean squared error (MSE) of 2.78731E−4, and 1.28701E−9, respectively. The training process was performed using two different sets of input data. In the first approach, all available well-logging data were used as input, ending up in high MSE. In the second approach, some selected well logs were used based on the results of sensitivity analysis which clearly improved the estimations. The ability of MLP networks made great improvements in the estimation of the both parameters up to 99.9%. The presence of valuable core data in this study significantly improved the process of comparison and conclusion. The final results prove that AI is a trusted method, also the potential of the ANN method for the reservoir characterization and evaluation associated problems should be taken into consideration. Due to the unavailability of core data along the whole wells, the application of intelligent methods, such as machine learning (ML) can be used to estimate the parameters in other oil or gas fields and wells.

Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow

Oil and gas field development optimization, which involves the determination of the optimal number of wells, their drilling sequence and locations while satisfying operational and economic constraints, represents a challenging computational problem. In this work, we present a deep-reinforcement-learning-based artificial intelligence agent that could provide optimized development plans given a basic description of the reservoir and rock/fluid properties with minimal computational cost. This artificial intelligence agent, comprising of a convolutional neural network, provides a mapping from a given state of the reservoir model, constraints, and economic condition to the optimal decision (drill/do not drill and well location) to be taken in the next stage of the defined sequential field development planning process. The state of the reservoir model is defined using parameters that appear in the governing equations of the two-phase flow (such as well index, transmissibility, fluid mobility, and accumulation, etc.,). A feedback loop training process referred to as deep reinforcement learning is used to train an artificial intelligence agent with such a capability. The training entails millions of flow simulations with varying reservoir model descriptions (structural, rock and fluid properties), operational constraints (maximum liquid production, drilling duration, and water-cut limit), and economic conditions. The parameters that define the reservoir model, operational constraints, and economic conditions are randomly sampled from a defined range of applicability. Several algorithmic treatments are introduced to enhance the training of the artificial intelligence agent. After appropriate training, the artificial intelligence agent provides an optimized field development plan instantly for new scenarios within the defined range of applicability. This approach has advantages over traditional optimization algorithms (e.g., particle swarm optimization, genetic algorithm) that are generally used to find a solution for a specific field development scenario and typically not generalizable to different scenarios. The performance of the artificial intelligence agents for two- and three-dimensional subsurface flow are compared to well-pattern agents. Optimization results using the new procedure are shown to significantly outperform those from the well pattern agents.

Oil Field Development Scenario With Geological Characterization In "Alpha" Field Of South Sumatra Basin

The Structure of Alpha Field located in South Sumatra Basin, Geographically located around Musi Banyu Asin, Sub-district Sungai Lilin. The target reservoir to be developed is Baturaja Formation, domination with sandstone, and consisting of 9 layers. To forecast the production of the field, it use decline curve analysis. From the existing oil production data, exponential and hyperbole charts can be created. So, the analysis of decline curve method can forecast how much hydrocarbon can be produce from the reservoir. To get the maximal recovery of oil production, field development can be done. In making a geological model or geological characterization of the Alpha field using the Oasis Montaj software. Based on the calculation results, scenario 1 which is the basecase, is unable to produce for 20 years. The cumulative value of production is 76,243.8 with an RF of 9.67%. Scenario 2 is basecase/scenario 1 plus several workover wells (WO) capable of producing for 20 years with a cumulative production value of 484,748.7 STB with an Recovery Factor (RF) value of 10.47%. Whereas scenario 3 is scenario 2 plus several workover wells with the addition of infill wells capable of producing for 20 years with a cumulative production of 2,036,907.1 STB with an RF value of 13.50%. Of the three scenarios, the best scenario is scenario 3 because it has the highest cumulative value of production and RF and is certainly capable of producing for 20 years after a simulation using the DeclineCurve Analysis method. There is a relationship between geological characterization and field development scenarios where to provide information about Alpha Field, especially which parts and wells have high productivity so that it can be used as a reference in field development, especially when determining drill points for infill wells and well workovers.

A machine learning model for predicting multi-stage horizontal well production

In this study, a hybrid convolutional-recurrent neural network (c-RNN) is evaluated for making predictions of the five-year cumulative production profiles in multistage hydraulically fractured wells. The model was trained by using a combinations of completion parameters, rock mechanical properties, and well spacing and completion order for each stage of 74 wells in the Montney Formation in Alberta. The prediction accuracy of the various combinations was measured by using the mean average percent error and mean absolute error generated through the leave-one-out method. The best combination of inputs was found to be the rock mechanical properties surrounding each perforation cluster, the proppant amount used for every stage, and the spacing and completion order of neighboring wells. The novelty of this study is that the input variables used are at the stage level rather than the average of the entire well. The accuracy of the model was found to increase exponentially as the production of multiple wells was aggregated. The approach yields insights for planning new well drills in fields with existing development since it provides the ability to run multiple field development scenarios without having to spend capital.

Reservoir simulation of Ulumbu geothermal field using TOUGH2 and ITOUGH2 simulator

Ulumbu geothermal field is classified as a natural two-phase geothermal system, located in the south of Mandosawu - Ranakah Old volcanics and Pocoleok, Flores Island. This field produces 4 x 2.5 MW electric power in 2014 until now with temperature between 230–240 °C. This study aims to perform reservoir simulation in the Ulumbu field. The reservoir simulation method aims to determine the reservoir characteristics of the geothermal field. This research used TOUGH2 and ITOUGH2 simulator to simulate the reservoir. The work concept from TOUGH2 is forward modeling while ITOUGH2 is inverse modeling. Both of methods will produce an output. The output of this research is natural state model that was validated by three wells of the actual measurement (pressure and temperature) and heat flow from the conceptual model. A pressure and temperature profiles are very representative with the data in the three wells. The reservoir location is between Poco Leok and Poco Rii (upflow zone) and continues to the southwest (outflow zone) with an average area of 10.5 km2. The top reservoir is at an elevation of 500 m, while the depth is up to -2000 m. Reservoir rock has a permeability value of around 10−14–10−15 m2, porosity value of 8–10 %, density value of 2500 kg/m3, and specific heat capacity value of 900 J/kgK. Temperature distribution results of the natural state model show the hydrogeological profile of the Ulumbu geothermal system. The natural state model is then used to help undertake a geothermal field development scenario, that is, to calculate resource reserves and to make recommendations for subsequent drilling zones.

The Application of Numerical Simulation Result for Geothermal Financial Model with Probabilistic Approach: A Comprehensive Study

Feasibility of developing a geothermal project depends on the financial return generated from the investment. One of the strategies to achieve optimum return is formulating a financial model with a high level of confidence. Technical input parameters in the financial model are determined by the amount of available geothermal reserve in the form of a field development scenario. The best method for predicting geothermal reserve is a numerical simulation. The objective of this study is to determine the electricity tariff to generate 30 MW, 60 MW, and 110 MW which meet the 50% of the Rate of Return value will be equal or not exceed 16% (P50) for a specific geothermal field with a probabilistic approach. This study started with determining the technical input parameters: the number of production wells; make­up wells; and injection wells from each development scenarios based on numerical simulation result that has been studied by another researcher. The electricity tariff that meets the P50 of Rate of Return at 16% was calculated for those scenarios. Then, the tariffs were evaluated based on the Average Cost of Electricity Generation (BPP) on the relevant local grid. The result shows that the tariff or/and generation cost need to be negotiated. Moreover, total investment and economic indicators forecasting indicated that the investment was attractive. Lastly, sensitivity analysis shows that Rate of Return strongly affected by well drilling cost and power plant cost (EPCC).

Olympus challenge—standardized workflow design for field development plan optimization under uncertainty

The “Olympus challenge” is defined as an open benchmark study on field development optimization under geological uncertainty. This work describes a structured approach to the Olympus challenge. It combines a systematic performance delivery analysis based on multiple reservoir model realizations with optimization strategies including well controls, field development scenarios, and combined strategies. The ambition of this work is to design practical and robust workflows integrating economic, reservoir geology, and delivery performance that can be applied to current real field studies. Probabilistic assessments on economic performance and reservoir opportunities are used for project framing and to define start points for optimization strategies. A sequential optimization strategy is applied to handle discrete control parameters with a time-dependent impact on economic performance over the life cycle of the reservoir. Probability maps are applied to identify reservoir opportunities and a probabilistic well ranking is introduced to investigate the robustness of a well location design. Objective measures for probabilistic evaluations are described and applied for result comparison between an optimized and a reference solution in the presence of multiple realizations. In conclusion, this work provides solution proposals for multiple optimization objectives of the Olympus challenge. Standardized workflow designs deliver manageable and repeatable work steps and give an outlook to automation.

FieldOpt: A powerful and effective programming framework tailored for field development optimization

Petroleum field development involves critical decisions such as well location, well completion design and optimal control setting that have a significant impact on revenues and costs. These decisions are associated with a large degree of engineering effort that commonly involves time-consuming reservoir simulations to compute the performance of different field development scenarios. Increasingly within the petroleum industry, software tools and optimization methodology are developed and implemented to support and augment the various decision-making processes. The overall aim of these tools is to increase productivity and improve decision quality. Within this context, this work introduces an open-source, extensible, tailor-made programming framework: FieldOpt. FieldOpt’s primary purpose is rapid prototyping and testing of optimization procedures to solve critical field development problems. The framework is implemented in C++ and provides an efficient integration of mathematical optimization procedures with reservoir simulation.FieldOpt’s modular architecture and use of object-oriented programming allow the users to adapt and extend the code with ease. The architecture has proven successful in facilitating new optimization algorithms, new use cases, and new methodology for optimization with minimal effort and change to internal data structures and data flow.Three use cases are presented to demonstrate the optimization capabilities of FieldOpt as well as the flexibility and ease-of-use of the software regarding configuring various optimization procedures to solve a range of field development problems. The three use cases presented optimize on well control, well completion design and well placement parameters, respectively. For each case, both the configuration of the algorithms and problems within FieldOpt as well as the final solution and performance of the different optimization procedures are discussed. In all three cases, FieldOpt was able to find significant improvements or crucial information to decision making.

Multiscale modeling of compartmentalized reservoirs using a hybrid clustering-based non-local approach

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach.A novel and rigorous non-local formulation for flow in porous media is presented, in which the reservoir is represented by an adjacency matrix describing the connectivities of comprising compartments. We automatically divide the reservoir into a number of distinct compartments, in which the direction-dependent multiphase flow communication is a function of non-local phase potential differences. Our proposed clustering framework begins with a mixed-type raw dataset which can be categorical/numerical, spatial/temporal, and discrete/continuous. The dataset can contain noisy/missing values of different data types including but not limited to well production/injection history, well location, well type, geological features, PVT measurements, perforation data, etc. Unsupervised clustering techniques suited to the input data types (e.g. k-prototypes, spectral, Gaussian Mixtures, and hierarchical clustering), and appropriate distance measures (such as Euclidean distance, soft dynamic time warping, and mode) are used. The input data is standardized, and upon convergence check, the best clustering representation is obtained. Finally, Support-Vector-Machine technique is utilized in the kernel space to trace a demarcating hyperplane for the clusters.In a specific case study reported here, the proposed workflow is applied to a major field with a couple of hundreds of wells with more than 40 years of production history. Leveraging the fast forward model, an efficient ensemble-based history matching framework is applied to reduce the uncertainty of the global reservoir parameters such as inter-blocks and aquifer-reservoir communications, fault transmissibilities, and block-based oil-in-place. The ensemble of history matched models are then used to provide a probabilistic forecast for different field development scenarios. In addition, the clustering framework enables us to treat missing data and use the augmented dataset for improving the clustering accuracy.In summary, in this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome.

Applying the hydrodynamic model to optimize the production for crystalline basement reservoir, X field, Cuu Long Basin, Vietnam

Weathered and fractured crystalline basement is known as the important unconventional reservoir in the Cuu Long Basin. Naturally fractured reservoir plays a crucial role in oil exploration to contribute for hydrocarbon production in Vietnam. However, the complexity and heterogeneity of the fractures system in the basement reservoir are challenges for oil and gas production. They require the realistic simulation scenarios to estimate the hydrocarbon potential as well as field development plan of these reservoirs. Thus, this paper aims to propose the feasibility development scenarios to improve oil recovery factor for crystalline basement reservoir, X field, Cuu Long Basin, Vietnam. First, history matching process is validated for the model to fit the actual production data (reservoir pressure, pressure, water cut in each well) in order to approach closer the fluid flow behavior through the reservoir. The manual matching was selected to adjust the actual aquifer size and permeability distribution with limit simulation runs. Next, the highest reliability matching model which approximately reflects the actual fluid flow behavior can be used as the base case to forecast the future reservoir performance through the field development plan. The most potential scenario is to add six new infill production wells, two side track wells and two water injection wells. The forecasted results indicate that this scenario yields 8% more oil recovery factor compared to the natural drive with thirteen producers. This result suggests that the precise field development plan is to increase the efficiency of the production process by increasing the displacement parameters of residual oil and reservoir sweep efficiency by stimulation. The major contribution of this paper demonstrates the merits of the field development plan in fractured basement reservoir. The findings of this study can help better understand the fluid flow behavior using the production history profiles and field development scenarios of crystalline basement reservoir of Cuu Long Basin.

Economic and productivity evaluation of different horizontal drilling scenarios: Middle East oil fields as case study

Development of high-density oil and gas fields presents a great challenge to the energy industry due to the low productivity of individual wells and their high drilling cost. We thus compared the productivity, associated costs and economical revenues gained from two field development scenarios, with multilateral and horizontal drilling, to evaluate the optimal drilling and completion conditions in a giant heavy oil reservoir in the Middle East. Well path design was identified as one of the most complex parameters depending on the well-testing results, field production and reservoir simulation data. The fishbone well of four branches with a length of 300 m each and 30° deviation from the main hole was identified to be drilled and completed using open-hole sidetrack as the best approach. The fishbone structure raised production by 393%, while drilling cost only increased by 130% compared with a conventional horizontal well.

Economic Evaluation of Y Gas Field Development Using Reservoir Simulator

Y Field which located around 28 km south east of Jakarta was discovered in 1989. Three wells have been drilled and suspended. The initial gas ini place (IGIP) of the field is 40.53 BSCF. The field will be developed in 2011. In this study, reservoir simulation model was made to predict the optimum development strategy of the field. This model consisted of 1,575,064 grid cells which were built in a black oil simulator. Two field development scenarios were defined with and without compressor. Simulation results show that the Recovery Factor at thel end of the contract is 61.40% and 62.14% respectively for Scenarios I and II without compressor. When compressor is applied then Recovey Factor of Scenarios I and II is 68.78% and 74.58%, correspondingly. Based on the economic parameters, Scenario II with compressor is the most attractive case, where IRR, POT, and NPV of the scenario are 41%, 2.9 years, and 14,808 MUS$.

Field development optimization in mature oil reservoirs using a hybrid algorithm

Many optimization schemes have been proposed to simultaneously optimize various variables such as well locations, well operation schedules, well types, and the number of wells. However, most of these approaches often focused on fixed well type without considering conversion of existing wells.This paper proposes a new optimization for mature oil field development. Since converting from producers to injectors is a common practice in mature oil field, we have to optimize simultaneously type conversion schedules of all existing producers and infill wells as well as the number of infill wells, their locations, and operation schedules. We propose a new hybrid algorithm, which combines differential evolution (DE) algorithm and mesh adaptive direct search algorithm (MADS) to solve our optimization task.By considering well type conversion, it will increase the complexity of searching space but provide more realistic and optimal development plan. We demonstrate it in 2D synthetic and 3D PUNQ-S3 reservoirs for optimal field development. The proposed optimization considering well type conversion provides higher net present value than the fixed well type optimization in the both cases. The hybrid algorithm also shows better search performances than DE and MADS algorithms. Thus, we conclude that consideration of well conversion schedules is necessary for economical field development scenarios in mature oil fields.

Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Waterflooding Process

Assessment of diverse operational constraints and risk appraisal associated with reservoir heterogeneities are essential foundation of production optimization and oil field development scenarios. Water-flooding performance evaluation that comprises comprehensive numerical simulations is typically cumbersome in terms of time and money, which is not reasonably appropriate for practical decision making and future performance forecasting. Cognitive data-driven proxy modeling practices, which incorporate data-mining techniques and machine learning concepts, offer a fascinating substitute for explicit models of the underlying process that can be instantaneously reassessed, especially for extremely nonlinear system forecasts. In this paper, an exploratory data analysis is applied to create a comprehensive data set from Water-flooding actual field data, which entails different characteristics labeling reservoir heterogeneities and other pertinent operational constraints. Artificial neural network (ANN) is applied as a cognitive data-driven proxy modeling effort to predict Water-flooding production in heterogeneous reservoirs. This study presents the great potential of cognitive data-driven proxy modeling techniques for practical applications and as a feasible add on for investigating a huge quantity of real field data efficiently. In addition, the suggested methodology can be incorporated directly into most present reservoir development decision making routines.

Integration of reservoir simulation, history matching, and 4D seismic for CO2-EOR and storage at Cranfield, Mississippi, USA

In this paper, we compare 4D seismic interpretations of CO2 plume evolution with fluid-flow numerical simulation results for Cranfield, Mississippi. Historic pressure trends, oil and gas production rates, and current CO2-EOR production data from the field were history matched, and a tuned model was used for predictive simulations.For CO2-EOR operations, numerical simulation results of the CO2 plume distribution and CO2 first arrival (breakthrough) times in production wells were compared to the available field data. Three interpretations of 4D seismic data show discrepancies on the edges of the seismic survey, and along the sealing fault, where numerical simulations show high CO2 saturations. In areas between these two limits, the match between simulation and 4D seismic interpretation improves. In addition, for most of the production wells, comparison of the breakthrough time of CO2 showed a reasonable match.The tuned model was then used to predict reservoir response and storage capacity in different field development scenarios under CO2 injection. We compared hypothetical scenarios where the operator transitions from CO2-EOR to CO2 injection without oil production (CO2-EORT) when oil production is not economical anymore, to a scenario of continuing with CO2-EOR. Our results show that CO2-EOR can store more CO2 and operations will last longer, whereas if switched to CO2-EORT, the field must be abandoned earlier because of spillover of the CO2 plume. However, the amount of CO2 stored per year is larger for CO2-EORT as compared to CO2-EOR.

Hydraulic flow units, depositional facies and pore type of Kangan and Dalan Formations, South Pars Gas Field, Iran

It is highly essential to characterize the reservoir as rigorously as possible to implement enhanced oil recovery operations and field development scenarios. In this scope, use of novel concepts and techniques for classification of reservoir rocks and definition of producing and non-producing zones will be greatly helpful. In the current research, hierarchical clustering is employed to characterize hydraulic flow units of four wells of South Pars Gas Field using core data. Studying the clustering tree, also known as dendrogram, it is proved that six is the optimal number of flow units for reservoir zonation. Cross plot of porosity versus permeability for each flow unit shows high cross correlation value, which confirmed the accuracy of employed technique for grouping the FZI data. The available core data were studied to identify the possible microfacies. Ten micofacies are distinguished including massive to laminated anhydrite, evaporite bearing fenestral dolomudstone, pellet grainstone, pellet wackestone to packstone, bioclastic wackestone to packstone/mudstone, bioclastic wackestone to mudstone, bioclastic ooid grainstone, ooid grainstone, intraclastic grainstone, and thrombolite boundstone. The frequency of each facies for each hydraulic flow units was investigated. The results indicate that presence of anhydrate severely reduced reservoir quality. According to thin section and core slab analyses results, high quality flow units mainly consist of bioclastic ooid grainstone, ooid grainstone, and intraclastic grainstone and thrombolite boundstone, which are associated with shoal facies. Finally, thin section and scanning electron microscope (SEM) analysis was done to recognize pore types of each flow unit.

Estimating effective shale area distributions from subsurface data

Estimation of shale bed continuity is needed in reservoir modeling and simulation as these constitute permeability barriers and baffles. However, efforts to characterize continuity of shale beds from 2D outcrop or well log sections lack critical 3D geometry and areal dimensions. A different approach is to estimate shale bed continuity by measuring the “effective shale area”. While it is a more indirect measure of shale bed continuity as it relies upon the sealing capacity of shales in selected, well-documented field examples, it eliminates the problem of the unknown but critical third dimension. Analyses of effective shale area distributions are made within structurally simple fields where hydrocarbon contacts were directly controlled by the sealing shales being measured, and faulting is limited. Datasets are exclusive to siliciclastic reservoirs formed in paleoenvironments ranging from non-marine to shallow water marine.Results show that effective shale areas are separated into two distinct distributions of fully marine versus non-marine/marginal marine origin. Shales deposited in non-marine to marginal marine paleoenvironments display smaller areas where effective sealing capacity is reduced by channel body incision. In fields where hydrocarbon columns are sealed by fully marine shale, larger areas are supported, often but not always, approaching structural spill points. In the case of reservoirs capped by these marine flooding shales, one can logically infer an abrupt decrease in channelization and resulting sizeable increase in continuity of shale bed seals.Similar shale types in traps formed in different structural regimes (e.g. extensional versus contractional domains) show comparable areal distributions, supporting use of data from different locations where the controls on hydrocarbon seal areas are well known. All shales measured here are areally large and would probably be accommodated in reservoir simulation deterministically, but use of the dimensions provided here would clearly be an aid in situations where a limited number of field appraisal wells are available. Discriminating between deterministic shales and stochastic shales, which are several orders of magnitude smaller in size, is also important for reservoir modeling and simulation. Results shown here should also be of great utility as input into discovery appraisal and field development scenario construction.