Prediction of Structure-H Gas Hydrate Formation Conditions for Reservoir Fluids

In this work, a thermodynamic model is developed for prediction of structure H hydrate formation. The model combines the Peng-Robinson equation of state for the vapor, liquid and aqueous phases with the extended Ng-Robinson hydrate model for gas hydrate formation of all three structures. The parameters of 14 structure- H hydrate formers are determined based on the experimental data of structure-H hydrates in the literature. The expression of fugacity of water in the empty hydrate phase is correlated for calculating structure-H hydrate formation conditions in the absence of free water. The model is tested by predicting hydrate formation conditions of a number of structure-H hydrate forming systems which are in good agreement with the experimental data. The proposed model is also applied to the prediction of hydrate formation conditions for various reservoir fluids such as natural gas and gas condensate.

A new method for reservoir fluid identification

The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, and pore structure. We have developed a new method based on WT energy spectra analysis, by which the signal component reflecting the reservoir fluid property is extracted. We have successfully processed real log data from an oil field in central China using this method. The results of the reservoir fluid identification agree with the results of well tests.

Frequencies of Lock Gate Structure Coupled with Reservoir Fluid

This study determines the natural frequencies of the lock gate structure,considering the coupled effect of reservoir fluid on one side using the finite element method(FEM).The gate is assumed to be a uniformly thick plate,and its material is isotropic,homogeneous,and elastic.The reservoir fluid is assumed to be inviscid and incompressible in an irrotational flow field.The length of the reservoir domain is truncated using the far boundary condition by adopting the Fourier series expansion theory.Two different assumptions on the free surface,i.e.,undisturbed and linearized,are considered in the fluid domain analysis.The computer code is written based on the developed finite element formulations.The natural frequencies of the lock gate are computed when interacting with and without reservoir fluid.Several numerical problems are studied considering the effects of boundary conditions,aspect ratios,and varying dimensions of the gate and the fluid domain.The frequencies of gate reduce significantly due to the presence of fluid.The frequencies increase when the fluid extends to either side of the gate.The frequencies reduce when the depth of the fluid domain above the top edge of the gate increases.The frequencies drop considerably when the free surface condition is taken into account.The results of frequencies of lock gate structure may be useful to the designer if it is experienced in natural catastrophes.

Fluid identification based on frequency-dependent AVO attribute inversion in multi-scale fracture media

A key problem in seismic inversion is the identification of the reservoir fluids. Elastic parameters, such as seismic wave velocity and formation density, do not have sufficient sensitivity, thus, the conventional amplitude-versus-offset（AVO） method is not applicable. The frequency-dependent AVO method considers the dependency of the seismic amplitude to frequency and uses this dependency to obtain information regarding the fluids in the reservoir fractures. We propose an improved Bayesian inversion method based on the parameterization of the Chapman model. The proposed method is based on 1） inelastic attribute inversion by the FDAVO method and 2） Bayesian statistics for fluid identification. First, we invert the inelastic fracture parameters by formulating an error function, which is used to match observations and model data. Second, we identify fluid types by using a Markov random field a priori model considering data from various sources, such as prestack inversion and well logs. We consider the inelastic parameters to take advantage of the viscosity differences among the different fluids possible. Finally, we use the maximum posteriori probability for obtaining the best lithology/fluid identification results.

Burial History and Petroleum Entrapment in the Yaoyingtai Region of the Changling Fault Depression, China

This work aims to reconstruct the burial history of various kinds of sandstones penetrated by the well YS201 in the Yaoyingtai region of the Changling Fault Depression, southern Songliao Basin, China. Analyses of fluid inclusions in the reservoir rocks, combined with a review of the regional tectonic evolution, revealed the hydrocarbon accumulation stage and accumulation age of the Early Cretaceous Denglouku group and the first member of the Quantou group reservoir, which are the future exploration focus for deep gas reservoirs in this region. Acoustic time data and sedimentary rates calculated for sediments in the YS201 well yielded thicknesses for the Yingcheng, Nenjiang, and Mingshui groups of 506, 539.18, and 144.85 m, respectively, thereby revealing the burial history of the sediments in the well. Fluid inclusions of the Denglouku group reservoir and the first member of the Quantou group reservoir contain oil inclusions and hydro-carbonaceous salt water inclusions. The main peaks of the homogenization temperature and salinity of these saltwater inclusions in the first member of the Quantou group reservoir are generally 110-120℃ and 6wt%-8wt%, respectively, and for the Denglouku group are 130-140oc and 4wt%-6wt%. The data for both reservoirs show only one main peak, indicating that they both have experienced single-stage accumulation. Combining the homogenization temperature of the reservoir fluid inclusions with the burial and thermal history of the sediments in the YS201 well, we infer that the hydrocarbon gas in these two intervals accumulated at 79 Ma （middle Late Cretaceous）.

Calculation and application of partition coefficients of light hydrocarbons in oil-based mud system

To find out the relationship between the oil-based mud,the formation fluid and the extracted gas,we use a thermodynamic approach based on static headspace gas chromatography technique to calculate the partition coefficients of 47 kinds of light hydrocarbons compounds between nC5 and nC8 in two kinds of oil-based mud-air systems,and reconstruct the original formation fluid composition under thermodynamic equilibrium.The oil-based drilling mud has little effect on the formation fluid compositions in the range of nC5-nC8(less than 1%for low-toxicity oil-based mud and less than 10%for oil-based mud).For most light hydrocarbon compositions,the partition coefficients obtained by vapor phase calibration and the direct quantitative methods have errors of less than 10%,and the partition coefficients obtained by direct quantitative method are more accurate.The reconstructed compositions of the two kinds of crude oil have match degrees of 91%and 89%with their real compositions,proving the feasibility and accuracy of reconstructing the composition of original formation fluid by using partition coefficients of light hydrocarbon compositions between nC5 and nC8.

Data driven prediction of oil reservoir fluid properties

Accuracy of the fluid property data plays an absolutely pivotal role in the reservoir computational processes.Reliable data can be obtained through various experimental methods,but these methods are very expensive and time consuming.Alternative methods are numerical models.These methods used measured experimental data to develop a representative model for predicting desired parameters.In this study,to predict saturation pressure,oil formation volume factor,and solution gas oil ratio,several Artificial Intelligent(AI)models were developed.582 reported data sets were used as data bank that covers a wide range of fluid properties.Accuracy and reliability of the model was examined by some statistical parameters such as correlation coefficient(R2),average absolute relative deviation(AARD),and root mean square error(RMSE).The results illustrated good accordance between predicted data and target values.The model was also compared with previous works and developed empirical correlations which indicated that it is more reliable than all compared models and correlations.At the end,relevancy factor was calculated for each input parameters to illustrate the impact of different parameters on the predicted values.Relevancy factor showed that in these models,solution gas oil ratio has greatest impact on both saturation pressure and oil formation volume factor.In the other hand,saturation pressure has greatest effect on solution gas oil ratio.

Lattice Boltzmann simulation of fluid flow through coal reservoir's fractal pore structure

The influences of fractal pore structure in coal reservoir on coalbed methane(CBM) migration were analyzed in detail by coupling theoretical models and numerical methods.Different types of fractals were generated based on the construction thought of the standard Menger Sponge to model the 3D nonlinear coal pore structures.Then a correlation model between the permeability of fractal porous medium and its pore-size-distribution characteristics was derived using the parallel and serial modes and verified by Lattice Boltzmann Method(LBM).Based on the coupled method,porosity(ф),fractal dimension of pore structure(Db),pore size range(rmin,rmax) and other parameters were systematically analyzed for their influences on the permeability(ф) of fractal porous medium.The results indicate that:① the channels connected by pores with the maximum size(rmax) dominate the permeability,approximating in the quadratic law;② the greater the ratio of r max and r min is,the higher is;③ the relationship between D b and follows a negative power law model,and breaks into two segments at the position where Db ≌2.5.Based on the results above,a predicting model of fractal porous medium permeability was proposed,formulated as k=cfrnmax,where C and n(approximately equal to 2) are constants and f is an expression only containing parameters of fractal pore structure.In addition,the equivalence of the new proposed model for porous medium and the Kozeny-Carman model k=Crn was verified at Db =2.0.

Geostress effect on resistivity and its relevant correction method

For reservoirs with abnormally high pressure and high geostress,formation resistivity can be greatly affected.This increase of resistivity resulting from high stress causes errors in the identification of reservoir fluids.In order to investigate the effect of stress on resistivity,resistivity measurement was conducted simultaneously with triaxial testing to obtain rock resistivity under high temperature and high pressure.The changes of resistivity and resistivity increasing coefficient with horizontal differential stress and minimum horizontal stress were revealed from experiments.Besides,field data were analyzed to show the main influencing factors of formation resistivity under reservoir conditions.In addition,a new resistivity correction model for high geostress formation was derived in this work.The interpretation results are in good agreement with well testing data in the Keshen area of the Tarim oilfield,China.

Toward connectionist model for predicting bubble point pressure of crude oils: Application of artificial intelligence

Knowledge about reservoir fluid properties such as bubble point pressure(Pb)plays a vital role in improving reliability of oil reservoir simulation.In this work,hybrid of swarm intelligence and artificial neural network(ANN)as a robust and effective method was executed to determine the Pb of crude oil samples.In addition,the exactly precise Pb data samples reported in the literatures were employed to create and validate the PSO-ANN model.To prove and depict the reliability of the smart model developed in this study for estimating Pb of crude oils,the conventional approaches were applied on the same data set.Based on the results generated by PSO-ANN model and other conventional methods and equation of states(EOS),the PSO-ANN model is a reliable and accurate approach for estimating Pb of crude oils.This is certified by high value of correlation coefficient(R2)and insignificant value of average absolute relative deviation(AARD%)which are obtained from PSO-ANN outputs.Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.