Oil Formation Volume Factor Research Articles

Introducing a New Method for Predicting PVT Properties of Iranian Crude Oils by Applying Artificial Neural Networks

In this study artificial neural networks (ANNs) have been applied for the prediction of main pressure, volume, and temperature (PVT) properties, bubble point pressure (Pb), and bubble point oil formation volume factor (Bob) of crude oil samples from different wells of Iranian oil reservoirs. Via a detailed comparison, the great power of ANNs with respect to traditional methods of predicting PVT properties, like Standing, Vasquez and Beggs, and Al-Marhoun, with higher prediction precision up to R2 = 0.990 has been illustrated and the obtained parameters of ANNs for the application of prediction of other crude oil samples has been presented. The applied PVT data set in this study consists of 218 crude oil samples from Iranian reservoirs and for assurance of the applicability of the ANN model the PVT data set has been divided into 2 training (190 samples) and cross validation (28 samples) data sets and obtained ANNs from applying the training data set has been tested on the cross validation data set which has not been seen by the network during the training process. The obtained results for both training and cross validation data sets confirm the great prediction power of ANNs, for both data sets with respect to traditional PVT correlations.

Modeling of Compositional Grading and Plus Fraction Properties Changes with Depth in Petroleum Reservoirs

In this work we use a nonisothermal model to predict compositional variation in a petroleum fluid column; at the same time, a nonisothermal model will be used to predict change of plus fraction molecular weight (and as a result all other its properties) with depth. We will investigate the effect of several factors as mode of characterization, binary interaction coefficients, volume shifts, and near-criticality on the change of physical properties, including bubble point pressure, solution gas–oil ratio, and oil formation volume factor. A computer program will be used to predict the location of the gas–oil contact and we will show how neglecting compositional grading (CG) can lead to serious errors in the calculated initial oil in place.

Modeling the correlations of crude oil properties based on sensitivity based linear learning method

This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crude oil systems using sensitivity based linear learning method (SBLLM). PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties, such as bubble-point pressure and oil formation volume factor, is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially their instability and inconsistency during predictions. In this paper, a sensitivity based linear learning method (SBLLM) prediction model for PVT properties is presented using three distinct databases while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. In the formulation used, sensitivity analysis coupled with a linear training algorithm for each of the two layers is employed which ensures that the learning curve stabilizes soon and behaves homogenously throughout the entire process operation. In this way, the model will be able to adequately model PVT properties faster with high stability and consistency. Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling.

PVT correlations for Indian crude using artificial neural networks

Correlations for bubble point pressure, solution gas–oil ratio (GOR), oil formation volume factor (OFVF) (for both saturated and undersaturated crude) and viscosity (for both saturated and undersaturated crude) have been developed for Indian (west coast) crude using Artificial Neural Networks (ANN). Detailed comparison has also been made with various important correlations currently available in the literature. Sensitivity analysis of the developed models was also performed to determine the relative importance of various input parameters. The training scheme used here is different from those used previously for developing ANN models. Bayesian regularization technique was used to ensure generalization and prevent over fitting. Also genetic algorithm (real coded with parent-centric crossover) was used coupled with a local optimizer (Marquardt–Levenberg) to obtain the global optimum network weights. It was found that the developed models outperformed most other existing correlations by giving significantly lower values of average absolute relative error for the parameters studied. This study shows highly favorable results which can be integrated in most reservoir modeling software.

NEW CORRELATION FOR OIL FORMATION VOLUME FACTOR AT AND BELOW BUBBLE POINT PRESSURE

The best source of oil properties data is the laboratory PVT analysis of a reservoir fluid sample. However, in the absence of experimentally measured properties of reservoir fluids, these physical properties must be estimated from correlations. This paper employs more than thirty PVT reports that have been taken from different Iraqi fields. These reports contain about four hundred experimental points.The paper suggests new correlation to calculate oil formation volume factor at and below bubble point pressure. All of the previous correlations did not take the pressure as a factor or independent variable because they calculate the oil formation volume factor at bubble point pressure only. The new correlation introduce the pressure while omit the solution gas-oil ratio which has been employed in all previous correlations. The accuracy of the proposed correlation of the experimental data is assessed through various statistical tests and comparing them with those achieved for some published correlations. These tests show that the new correlation has the best fitting with the experimental data for Iraqi oils. Cross plot technique is also applied to check the performance of the correlation and it gave the same index of the statistical criteria method. The new correlation reported absolute average error of 1.876%, sum of squared residuals of 0.395327, variance of 0.0093 and standard deviation error of 0.096337288.

PVT Correlations of Indian Crude Using Support Vector Regression

Correlations for bubble point pressure, solution gas−oil ratio, oil formation volume factor (for both saturated and undersaturated crude) and viscosity (for both saturated and undersaturated crude) have been developed for Indian crude using support vector regression (SVR). Detailed comparisons have been made with various important correlations currently available in the literature. Radial basis function (RBF) kernel was used along with ε-insensitive loss function for developing the SVR models. The model hyperparameters were optimized using a combination of grid search and Nelder−Mead simplex algorithm. The quadratic programming (QP) problem resulting from the SVR formulation was solved using sequential minimal optimization (SMO). It was found that the developed models outperformed most other existing correlations by giving significantly lower values of average absolute relative error for the parameters studied. This study shows highly favorable results that can be integrated in most reservoir modeling software.

Uncertainty and the Volumetric Equation

Guest editorial Every few years, the topic of correct inputs for use in the volumetric equation re-emerges. Our first recollection is from the 1997 SPE Hydrocarbon Evaluation and Economics Symposium when a dozen or so attendees remained after a session to discuss a logical flaw in a technical paper. All of those present agreed that they had been doing volumetric estimates incorrectly, due to their choice of distributions, and vowed to correct the situation. More recently, in the spring and summer of 2007, SPE's Reserves and Economics Technical Interest Group had a lively discussion on the same topic that ended without resolution and with extensive misunderstanding, in part because of perceived repetition of arguments. Now with the recent release of new reserves definitions and disclosure rules from the US Securities and Exchange Commission and comparisons with the Petroleum Resources Management System developed by SPE and other organizations, we may see more companies using probabilistic methods to estimate hydrocarbons in place, resources, and reserves, so we believe that it would be useful to reiterate the conclusion of those present at that 1997 meeting in Dallas. The crux of the issue is: What is the correct mathematical basis for establishing appropriate uncertainty distributions as inputs to a probabilistic analysis of the volumetric equation? More specifically: Should the input distribution for each reservoir parameter be based on eitherThe range of values seen in the well data for that parameter, or,The range of uncertainty in the reservoir average for that parameter? A Simple Model For illustrative purposes, consider using the volumetric equation in the form: oil initially in place (OIIP)=7758Ahφ(1−Sw)/Bo. The following discussion would also apply to other forms of the equation, such as using gross rock volume and net-to-gross ratio, for example. The total area of the reservoir, A, is 1,920 acres and 24 wells have been drilled on the basis of 80 acre spacing. The net pay, h, varies between 11 and 165 ft in the wells drilled to-date (Table 1) and statistical analysis of the net pay in the 24 wells shows that the average (mean) net pay is 81.5 ft. For the full calculation of OIIP, we would also need to consider uncertainties in area, porosity, water saturation, and the oil formation volume factor, but we will focus on net pay here.

Assessment Of Bubblepoint Oil Formation Volume Factor Empirical PVT Correlations

The oil formation volume factor (FVF) among other factors is the most important factor that enables the calculation of the amount of reserves in a particular reservoir. In this paper, the existing oil formation volume factor correlations were assessed for their performance using data from the Niger Delta Region. Two hundred and fifty PVT reports were validated for this study from various oil fields. Both quantitative and qualitative analytical methods were implemented through statistical parameters and performance plots respectively. From the general evaluation i.e. using the full range of the data, Hemmati and Kharrat (2007) correlation performed the best for the Niger Delta crude with percent mean absolute relative error (Ea) of 1.9055 and correlation coefficient (r) of 0.9897. From the oil API gravity ranges reliabilityanalysis, it is clear that different correlations other than Hemmati and Kharrat (2007) would be more appropriate for API≤ 35.

An Accurate Method for Determining Oil PVT Properties Using the Standing-Katz Gas Z-Factor Chart

SummaryAn accurate technique for estimating oil pressure/volume/ temperature (PVT) properties using the Standing-Katz Z-factor chart has been developed. Prior work to derive tuned gas-pseudocritical-property values that provide consistent and accurate compressibility factors has been extended to oils.Data from 1,099 worldwide oil PVT reports with 11,960 density measurements have been analyzed. The oils studied have API gravities ranging from 10.6 to 63, with gas/oil ratios ranging from 5 to 4,631 scf/STB. Bubblepoint formation volume factors (FVFs) range from 0.98 to 4.58 bbl/STB. Saturation pressures ranged from 60 to 10,326 psia at temperatures ranging from 50 to 332°F with pressure differentials to 18,491 psi.The resulting work shows that oil pseudocritical properties can be correlated with molecular weight, as with gases. The resulting relationships can be used to determine saturated-and undersaturated-oil density accurately. Oil PVT properties, such as FVF and isothermal compressibility, can then be derived from fundamental relationships with density.Because the new method correlates properties with the molecular weight of the wellstream, the chemical nature of the oil (e.g., whether naphthenic or paraffinic) is considered. Existing correlations (30 for bubblepoint oil FVF and 17 for isothermal compressibility) have been identified in the literature. These methods are tested against the database along with the new method to determine accuracy and recommended procedures. Isothermal oil compressibility has proved difficult to correlate historically, with errors typically in excess of 30%. The proposed method significantly reduces the error in calculated isothermal compressibility in comparison to traditional methods.

Comparison of the Performance of Empirical Models Used for the Prediction of the PVT Properties of Crude Oils of the Niger Delta

A review of the literature has revealed the lack of a formal analysis of the performance of empirical methods for the prediction of pressure volume temperature (PVT) properties of Niger delta crude oils. This study presents an assessment of the predictive accuracy of five bubble-point pressure (P b ) correlations and five bubble-point oil formation volume factor (B ob ) correlations against a large measured PVT data bank from Niger Delta crude oils. Statistical analysis techniques were used to evaluate the performance and the accuracy of the commonly used empirical models for estimating PVT properties of Niger crude oil in order to guide designers and operators in selecting the best correlations for their particular applications. Agreement between calculated and measured P b and B ob values for the various models was very poor. The model predictions of P b and B ob can be different from the measured values by 56% and 242%, respectively. Development of improved models for predicting the PVT properties of Niger delta crude oils is urgently required.

Estimating the Bubble Point Pressure and Formation Volume Factor of Oil Using Artificial Neural Networks

AbstractThe phase performance of hydrocarbons is a very complicated behavior that hydrocarbons show at the time of phase change or when they remain in a particular phase. Process design is almost impossible without a good understanding of this behavior. Artificial Neural Networks have been widely utilized for engineering applications during the last two decades. Two models are presented for the prediction of the bubble point pressure and the oil formation volume factor for hydrocarbon mixtures using the Artificial Neural Networks (ANNs) approach. For this purpose, five‐layer neural networks were designed and trained using 106 experimental data points. After the training step, 9 experimental data points were also used for the model evaluation step and as a reliability check. The output of the models for both the training and predicted data are compared with the empirical equations of Standing, Glaso and Marhoun. It is concluded that the ANNs approach has an excellent capability for these purposes compared to the conventional methods.

Novel Techniques for Measuring Heavy-Oil Fluid Properties

Summary Investigating the properties of live heavy oil, as pressure declines from the original reservoir pressure to ambient pressure, can aid in interpreting and simulating the response of heavy-oil reservoirs undergoing primary production. Foamy oil has a distinctly different and more complex behavior compared to conventional oil as the reservoir pressure depletes and the gas leaves solution from the oil. Solution gas separates very slowly from the oil; thus, conventional pressure/volume/temperature (PVT) measurements are not trivial to perform. In this paper, we present novel experiments that utilize X-ray computerized assisted technology (CT) scanning and low field nuclear magnetic resonance (NMR) techniques. These nondestructive tomographic methods are capable of providing unique in-situ measurements of how oil properties change as pressure depletes in a PVT cell. Specifically, this paper details measurements of oil density, oil and gas formation volume factor, solution gas/oil ratio, (GOR), and oil viscosity as a function of pressure. Experiments were initially performed at a slow rate, as in conventional PVT tests, allowing equilibrium to be reached at each pressure step. These results are compared to non-equilibrium tests, whereby pressure declines linearly with time, as in coreflood experiments. The incremental benefit of the proposed techniques is that they provide more detailed information about the oil, which improves our understanding of foamy-oil properties.

Monte Carlo Simulation of Oil Fields

Most investments in the oil and gas industry involve considerable risk with a wide range of potential outcomes for a particular project. However, many economic evaluations are based on the “most likely” results of variables that could be expected without sufficient consideration given to other possible outcomes, and it is well known that initial estimates of all these variables have uncertainty. The data is usually obtained during drilling of the initial oil well, and the sources are geophysical (seismic surveys) for formation depths and the areal extent of the reservoir trap, well logs for formation tops and bottoms, formation porosity, water saturation and possible permeable strata, core analysis for porosity and saturation data, and others. The question is how certain are the values of these variables and what is the probability of these values to occur in the reservoir to evaluate the possible risks? One of the most highly appreciable applications of the risk assessment is the estimation of volumetric reserves of hydrocarbon reservoirs (Monte Carlo). In this study, predictions were made about how statistical distribution and descriptive statistics of porosity, thickness, area, water saturation, recovery factor, and oil formation volume factor affect the simulated original oil in place values of two different oil fields in Turkey, and the results are discussed.

Estimation of Expected Monetary Values of Selected Oil Fields

As there is uncertainty in estimates of capital, reserves, and net present value in the petroleum industry; risk analysis is the key point for an oil company. It is easy to make decisions after quantifying the uncertainty with ranges of possible values and associated probabilities. Instead of deterministic models, probabilistic evaluations give a wide range of outcomes for decision making. Monte Carlo Simulation is a tool that presents different scenarios and yields probability and value relationships in reserve evaluations. In this study, an estimation of the reserves of two Turkish oil fields will be performed by using Monte Carlo Simulation and method of moments. Field data will be evaluated in two different programs. One of them is a Petroleum Risk Assessment program named CashPot, which is designed to assist in determining the economic feasibility of oil and gas exploration and development projects, and the other one is the Risk Analysis and Decision Making Program sponsored by U.S. Department of Energy. In the present work, predictions were made about how statistical distribution and descriptive statistics of porosity, thickness, area, water saturation, recovery factor, and oil formation volume factor affect the simulated original oil in place (OOIP) values. The current work presents the case of two different oil fields in Turkey. It was found that both techniques produce similar results for 95%. The difference between estimated values increases as percentages decrease from 50% and 5% probability.

Measurement and modeling of methane dissolution in synthetic liquids applied to drilling fluid formulation for deep and ultradeep water wells

Most of the Brazilian oil and gas reserves are located in offshore fields. There is also a great production potential in offshore reservoirs in deep and ultradeep water depths. The drilling scenario at those depths is quite complex and the risk of blowouts should be mitigated. A well control problem would incur in loss of human lives, equipment and catastrophic environmental damages. In order to overcome technical and environmental restrictions, synthetic liquids have been applied in the formulation of drilling fluids. Synthetic based drilling fluids show excellent lubricity and shale stabilization characteristics and may be the only option when drilling in high temperature and high pressure environments. The better understanding of the interaction between formation gas and a synthetic based drilling fluid during a kick detection and circulation situation would contribute to safely and economically drill the offshore wells. The present work involves PVT measurements and thermodynamic modeling of three mixtures of methane and organic liquids (n-paraffin, ester and a iso-paraffin : n-paraffin mixture), currently applied in drilling fluid systems for deep and ultradeep water drilling, in Campos Basin (Rio de Janeiro, Brazil). The measurement of thermodynamic properties of the methane–liquid mixtures, such as bubble point pressure, solubility, oil formation volume factor, gas formation volume factor and liquid density, were performed for 70 and 90 °C temperatures. The measurements were conducted in two different apparatuses: an Hg system and an Hg-free PVT system. The thermodynamic modeling of the gas–liquid system was based on Krichevsky–Kasarnovsky correction of Henry's law. The results showed that the precise accounting of formation gas solubility in the fluid under downhole conditions and during the kick circulation is a very important issue for safely drill deep and ultradeep water wells.

Accurate Estimation of the World Crude Oil PVT Properties Using Graphical Alternating Conditional Expectation

The accurate determination of the pressure, volume, and temperature (PVT) properties such as bubble-point pressure and oil formation volume factor is important in the primary and subsequent development of an oil field. These two parameters are essential for all petroleum engineering calculations such as reservoir simulations, recovery estimates, material balance calculations, well completion, facility design decisions, and production optimization strategies. In this study, a new approach is presented for predicting bubble-point pressure and oil formation volume factor for crude oil samples collected from different regions around the world. The regions include major oil-producing fields in North and South America, the North Sea, Southeast Asia, the Middle East, and Africa. The new approach, which is based on nonparametric optimal transformations, is called alternating conditional expectation (ACE). The transformations are totally data-driven and do not assume any a priori functional form. The data set used in the study consists of 5200 points that represent worldwide crude. An additional 200 PVT data sets were used to investigate the effectiveness of the new proposed method to predict outputs from inputs that were not used during the training process. The ACE model is able to predict the bubble-point pressure and oil formation volume factor as a function of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. The excellent results obtained from the proposed model establish a new simple tool for calculation of the two properties. The accuracy of the models developed in this study was compared in detail with several published correlations.

Role of reservoir engineering in the assessment of undiscovered oil and gas resources in the National Petroleum Reserve, Alaska

The geology and reservoir-engineering data were integrated in the 2002 U.S. Geological Survey assessment of the National Petroleum Reserve in Alaska (NPRA). Whereas geology defined the analog pools and fields and provided the basic information on sizes and numbers of hypothesized petroleum accumulations, reservoir engineering helped develop necessary equations and correlations, which allowed the determination of reservoir parameters for better quantification of in-place petroleum volumes and recoverable reserves. Seismic- and sequence-stratigraphic study of the NPRA resulted in identification of 24 plays. Depth ranges in these 24 plays, however, were typically greater than depth ranges of analog plays for which there were available data, necessitating the need for establishing correlations. The basic parameters required were pressure, temperature, oil and gas formation volume factors, liquid/gas ratios for the associated and nonassociated gas, and recovery factors. Finally, the results of U.S. Geological Survey deposit simulation were used in carrying out an economic evaluation, which has been separately published.

PVT Properties and Viscosity Correlations for Gulf of Mexico Oils

Summary New empirical pressure/volume/temperature (PVT) correlations for Gulf of Mexico (GOM) oils have been developed as a function of commonly available field data. Correlations have been developed for the following: Bubblepoint pressure. Solution gas/oil ratio (GOR) at bubblepoint pressure. Oil formation volume factor (FVF) at bubblepoint pressure. Undersaturated isothermal oil compressibility. Dead-oil viscosity. Saturated-oil viscosity. Undersaturated-oil viscosity. For the development of correlations, we have covered a wide range of data. More than 100 PVT reports from the GOM have been used in the development of correlations. Two of the published correlations (those of Standing1 and Petrosky and Farshad2) were tested with our GOM data set. Proposed correlations of this study predicted the PVT properties of GOM oils better than the correlations published in the literature, even when the coefficients of the published correlations are tuned. Using the correlations of this study, we have written a simple program that can generate PVT data sets for use in reservoir simulation. Laboratory-measured differential-liberation data for two different oil samples were also compared against our empirically generated differential-liberation data.

Multiphase Pump Recovers More Oil in a Mature Carbonate Reservoir

A field test for a multiphase pump (MPP) was conducted in Abqaiq field, a mature carbonate reservoir in Saudi Arabia. Test results showed that large quantities of oil, water, and gas mixtures could be pumped over long distances without separating gas from the liquids. The helico-axial MPP successfully lowered backpressure, revived dead wells, and was able to transport as much as 75,000 B/D total fluid to a separation plant 10 km away. From test startup (8 June 2001) until 1 June 2002, the pump logged 5,160 operational hours (215 days). Economic payout was achieved rapidly with up to 12,000 B/D of incremental oil production. The technology offers an attractive alternative to constructing expensive gas/oil separation plants in remote areas. This paper presents a summary of the field test.

Evaluation of empirically derived PVT properties for Middle East crude oils

This study evaluates the most frequently used pressure–volume–temperature (PVT) empirical correlations to determine reservoir–fluid properties for Middle East crudes. The best available correlations were selected and their predictions compared with a large database of reservoir–fluid studies of samples representing all active areas of the Middle East. The comparison is based on statistical error analysis. This paper gives the best correlations for estimating: bubblepoint pressure, solution gas–oil ratio, oil formation volume factor at bubblepoint pressure, total formation volume factor, undersaturated oil compressibility, and undersaturated, saturated, and dead oil viscosities. Values of reservoir liquid and gas properties are often needed when detailed laboratory PVT data is not available. The published correlations that are not based on Middle East data perform poorly, but, when they are calibrated with this data, the performance improves.