New empirical scaling equations for oil recovery by free fall gravity drainage in naturally fractured reservoirs

Gas-oil gravity drainage is a recognized major contributor to production in fractured reservoirs. While various empirical and analytical methods have been proposed to model this process, many of them contain assumptions that are questionable or require parameters that are not accessible at the field level. The aim of this work is to provide new, easy-to-use scaling equations for estimating the recoverable oil through gravity drainage in naturally fractured reservoirs, considering the effects of resistance capillary pressure. To accomplish this, data from four oilfields undergoing gravity drainage, including rock properties (eight sets), block height (three sets), and fluid properties (four sets), were used to generate a wide range of recovery curves using a single porosity numerical simulation model. Aronofsky's and Lambert's functions were then utilized to match the generated recovery curves. Statistical analysis revealed that the Aronofsky's function is more accurate in replicating the recovery patterns, while the Lambert's function tends to overestimate the early-time oil recovery and underestimate the oil recovery at a later stage in the majority of cases. A sensitivity analysis was subsequently performed, revealing that parameters such as absolute permeability, viscosity of oil, height of block, gas and oil density, characteristics of relative permeability and capillary pressure curves and interfacial tension (IFT) influence the amount of time taken to achieve the final recovery. Of these parameters, absolute permeability has the most significant effect on the amount of time needed to attain the final recovery, while the effect of difference between oil and gas densities is the lowest. Consequently, two different expressions were developed using nonlinear multiple regression analysis of simulated gravity drainage data which can be combined with the Aronofsky model to substitute the rate convergence constant. The new scaling equations include the effects of capillary pressure and other relevant factors in gravity drainage simulations. Both forms show satisfactory accuracy, as evidenced by the statistical parameters obtained (R2 = 0.99 and MSE = 0.0019 for both established correlations). The new correlations were verified using a wide range of oilfield data and are expected to provide a better understanding of the recovery process in naturally fractured reservoirs.

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data(a case study, carbonate Asmari Formation, Zagros Basin, SW Iran)

Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.

Estimation of the current stress field and fault reactivation analysis in the Asmari reservoir, SW Iran

Knowing the current condition of the faults and fractures in a reservoir is crucial for production and injection activities.A good estimation of the fault reactivation potential in the current stress field is a useful tool for locating the appropriate spot to drill injection wells and to calculate the maximum sustainable pore pressure in enhanced oil recovery and geosequestration projects.In this study,after specifying the current stress state in the Gachsaran oilfield based on Anderson’s faulting theory,the reactivation tendency of four faults(F1,F2,F3,and F4)in the Asmari reservoir is analyzed using 3D Mohr diagrams and slip tendency factors.Results showed that all the faults are stable in the current stress state,and F2 has the potential to undergo the highest pore pressure build-up in the field.On the other hand,F3 has the proper conditions(i.e.,strike and dip referring to σHmax orientation)for reactivation.Stress polygons were also applied to show the effect of the pore pressure increase on fault stability,in a graphical manner.According to the results,the best location for drilling a new injection well in this part of the field is the NW side of F2,due to the lower risk of reactivation.It was found that both methods of 3D Mohr diagrams and slip tendency factors predict similar results,and with the lack of image logs for stress orientation determination,the slip tendency method can be applied.The results of such studies can also be used for locating safe injection points and determining the injection pressure prior to numerical modeling in further geomechanical studies.

A new methodology for grouping and averaging capillary pressure curves for reservoir models

Primary drainage capillary pressure data are usually correlatable with a 3D predictable property of grid cells.Accordingly,rock typing is normally performed based on an established correlation.Primary drainage as well as corresponding imbibition and/or secondary drainage capillary pressure curves are averaged to establish a saturation table for each rock type region in reservoir modeling.This study in-vestigates the reliability of this industry-accepted methodology,and has two main contributions.First,we show that if different types of capillary pressures are plotted against water saturation,comparing them might be highly misleading.We demonstrate that although primary drainage capillary pressure data may be plotted against water saturation,the imbibition and secondary drainage capillary pressure data should be plotted against imbibed water saturation.This would enable reservoir engineers to check whether rocks with similar primary drainage capillary pressures do or do not have similar imbibition/secondary drainage counterparts.Using this technique,rock quality can be also deduced from imbibition and secondary drainage capillary pressure curves.We use capillary pressure data measured on limestone and sandstone samples from the Asmari Formation in three Iranian oilfields to evaluate our technique.The second contribution of this study is the proposal of a new methodology for preparing capillary pressure curves for reservoir models.In our methodology,a grid cell can represent more than one rock type region,each specific to a saturation function.As a part of this methodology,we present new physically meaningful equations for averaging primary drainage,imbibition,and secondary drainage capillary pressure curves.

Prediction of pore volume compressibility by a new non-linear equation in carbonate reservoirs

Pore volume compressibility is an essential parameter in reservoir studies,as it plays a major role in recovery mechanisms.Over the past decades,many attempts have been made to establish a link between the pore compressibility and the porosity and other mechanical properties of the rock.Some scholars introduced analytical correlations between pore compressibility and rock mechanical properties,while others developed empirical formulas for estimating pore compressibility based on a porosity calculated by comparing nonlinear models to laboratory data.In this study,pore volume compressibility is measured on 55 carbonate samples and then applied to derive an empirical relationship between pore compressibility and porosity at each stress step,which is useful for predicting pore compressibility based on initial porosity.We take the net stress effect into account and derive an empirical correlation based on net effective stress and initial porosity to predict pore compressibility.In the end,we compare the measured pore compressibility with that predicted by the derived correlation and other non-leaner models,which indicates that the newly proposed non-linear equation outperforms those available in literature。

Formation evaluation and rock type classification of Asmari Formation based on petrophysical-petrographic data:A case study in one of super fields in Iran southwest

The Oligo-Miocene Asmari Formation is one of the most important hydrocarbon reservoirs in the Middle East.The oilfield under study is one of the largest oilfields in the Zagros basin with the Asmari Formation being the major reservoir rock.In this study,petrographic analyses,petrophysical data and neural network clustering techniques were used for identifying rock types in the Asmari reservoir.Facies analysis of the Asmari Formation in the study area has resulted in the definition of 1 clastic lithofacies and 14 carbonate microfacies types.Using petrophysical logs from 43 wells and their correlation with capillary pressure(Pc)curves,led to the recognition of 7 electrofacies(EF1-EF7).Microscopic evidence of Electrofacies group C1 and S1 show that the sedimentary facies of these electrofacies are most commonly found in restricted and shoal facies belts zone.Also,petrographic studies show that the sedimentary facies of C2,C3,C4,S2 and S3 were formed in the open marine,Lagoon,and Tidal flat facies belt zone of homoclinal ramp sedimentary environment during the Oligo-Miocene based on relative sea level changes respectively.The link between electrofacies and geological data indicated that both sedimentary and diagenetic processes controlled the reservoir quality of the Asmari Formation.Porosity,permeability and water saturation were used to estimate the reservoir quality of each electrofacies.EFs 1 and 2 with high porosity and permeability,low water saturation is considered as the best reservoir with regard to sedimentary textures(dolowackestone and dolograinstone)and the effect of diagenetic processes such as dolomitization processes.Vuggy,growth framework and interparticle porosities are major in EF-2,while the intercrystalline porosity is the major type in EF-1.EFs 3 and 4 show low values of porosity,permeability and high percentage of water saturations,which characterizes them as poor reservoir rocks.Finally,EF-5 is the only electrofacies in the siliciclastic parts of the Asmari reservoir,which is composed of rounded and well-sorted quartz grains that are slightly cemented.In sandstone electrofacies,electrofacies EF-5(S1),is the best type of sandstone reservoir rock and to move towards electrofacies EF-7(S3),will reduce reservoir quality.In carbonate electrofacies,also,electrofacies no 1,the best type of carbonate reservoir rock can be observed and move towards electrofacie number 4,lower quality of reservoir rocks is seen.

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

Fluid-flow measurements of petroleum can be performed using a variety of equipment such as orifice meters and wellhead chokes.It is useful to understand the relationship between flow rate through orifice meters(Qv)and the five fluid-flow influencing input variables:pressure(P),temperature(T),viscosity(μ),square root of differential pressure(ΔP^0.5),and oil specific gravity(SG).Here we evaluate these relationships using a range of machine-learning algorithms applied to orifice meter data from a pipeline flowing from the Cheshmeh Khosh Iranian oil field.Correlation coefficients indicate that(Qv)has weak to moderate positive correlations with T,P,andμ,a strong positive correlation with theΔP^0.5,and a weak negative correlation with oil specific gravity.In order to predict the flow rate with reliable accuracy,five machine-learning algorithms are applied to a dataset of 1037 data records(830 used for algorithm training;207 used for testing)with the full input variable values for the data set provided.The algorithms evaluated are:Adaptive Neuro Fuzzy Inference System(ANFIS),Least Squares Support Vector Machine(LSSVM),Radial Basis Function(RBF),Multilayer Perceptron(MLP),and Gene expression programming(GEP).The prediction performance analysis reveals that all of the applied methods provide predictions at acceptable levels of accuracy.The MLP algorithm achieves the most accurate predictions of orifice meter flow rates for the dataset studied.GEP and RBF also achieve high levels of accuracy.ANFIS and LSSVM perform less well,particularly in the lower flow rate range(i.e.,<40,000 stb/day).Some machine learning algorithms have the potential to overcome the limitations of idealized streamline analysis applying the Bernoulli equation when predicting flow rate across an orifice meter,particularly at low flow rates and in turbulent flow conditions.Further studies on additional datasets are required to confirm this.

A new framework for selection of representative samples for special core analysis

Special core analysis(SCAL)measurements play a noteworthy role in reservoir engineering.Due to the time-consuming and costly character of these measurements,routine core analysis(RCAL)data should be inspected thoroughly to select a representative subset of samples for SCAL.There are no comprehensive guidelines on how representative samples should be selected.In this study,a new framework is presented for selection of representative samples for SCAL.The foundation of this framework is using methods of PSRTI,FZI*(FZI-star)and TEM-function for the early estimation of petrophysical static,dynamic,and pseudo-static rock types at RCAL stage.The global hydraulic element(GHE)approach is benefitted and a FZI*-based GHE method(i.e.,GHE*)is presented for partitioning data.The framework takes into consideration different laboratory,reservoir engineering,geological,petrophysical and statistical factors.A carbonate reservoir case is presented to support our methodology.We also show that the current forms of Lorenz and Stratigraphic Modified Lorenz Plots in reservoir engineering are not appropriate,and present new forms of them.

Fractured reservoir history matching improved based on artificial intelligent

In this paper,a new robust approach based on Least Square Support Vector Machine(LSSVM)as a proxy model is used for an automatic fractured reservoir history matching.The proxy model is made to model the history match objective function(mismatch values)based on the history data of the field.This model is then used to minimize the objective function through Particle Swarm Optimization(PSO)and Imperialist Competitive Algorithm(ICA).In automatic history matching,sensitive analysis is often performed on full simulation model.In this work,to get new range of the uncertain parameters(matching parameters)in which the objective function has a minimum value,sensitivity analysis is also performed on the proxy model.By applying the modified ranges to the optimization methods,optimization of the objective function will be faster and outputs of the optimization methods(matching parameters)are produced in less time and with high precision.This procedure leads to matching of history of the field in which a set of reservoir parameters is used.The final sets of parameters are then applied for the full simulation model to validate the technique.The obtained results show that the present procedure in this work is effective for history matching process due to its robust dependability and fast convergence speed.Due to high speed and need for small data sets,LSSVM is the best tool to build a proxy model.Also the comparison of PSO and ICA shows that PSO is less time-consuming and more effective.

Investigating dynamic rock quality in two-phase flow systems using TEM-function: A comparative study of different rock typing indices

Constructing a reservoir simulation model requires an accurate definition of saturation functions,including capillary pressure and relative permeability data.Usually,these saturation functions are available only for a limited number of samples.Assigning saturation functions to the simulation model requires finding a clear relationship between them and a 3D predictable property of grid cells.Different indices are usually evaluated to describe such a relationship.It was recently shown that each petrophysical dynamic rock type should form similar True Effective Mobility(TEM)curves.In this study,TEMfunction was used to evaluate the reliability and performance of different rock typing indices in defining dynamic rock types.FZI,FZIM,MFZI,and FZI*(FZI-star)indices were considered and tested on a collection of laboratory-derived core-flooding data from an Iranian carbonate reservoir.Results showed that FZI-star had a better performance than FZI,MFZI,and FZIM.