Pressure-volume-temperature Data Research Articles

Production Optimization of an Oil Well by Restraining Water Breakthrough

This study investigates the well named X (for confidential reasons) of the field called Y which initially was productive with the natural energy of the reservoir of the oil in the absence of water. After a few years of production, water began to overflow excessively in the well. The goal of this paper is to maximize the oil production in an oil well X by reducing water ingress. The Pressure Volume Temperature (PVT) data, completion data, and reservoir data are analyzed via PIPESIM and Excel software by using the nodal analysis method to get the well performance and decline curve for predictions. Two scenarios are considered: firstly, to install an electric submersible pump (ESP) to activate the X well and secondly to make a new perforation. The ESP is installed at 11300 ft where the water production flow rate is 5586.264 STB/d and the oil production flow rate is 1396.566 STB/d. The new perforation is installed at 12038 ft where the water production flow rate is 277.1693 STB/d and the oil production flow rate is 5543.387 STB/d. To have the optimal parameters, the sensitivity analysis is applied to the flowline diameter and the wellhead pressure. The optimal parameter values obtained are 308.6128 STB/d for the water production flow rate and 5863.643 STB/d for the oil production flow rate. The new perforation is appropriate because this scenario allows water reduction, oil production maximization, profitability of 98086854 $, and a return on investment in 5 months during 16 years of production.

Fluid characterization of oil and gas fields: implications for fault and top seal integrity assessment

Integrity of fault and top seal is a key factor that affects hydrocarbon fluid phase distribution in the subsurface. Mapping fluid property distribution can therefore provide important tools towards assessing seal integrity and trapping mechanisms – two of the most critical elements in petroleum systems analysis towards optimzing exploration and development strategies. This is best achieved by proper fluid characterization that integrates fluid geochemistry and pressure–volume–temperature (PVT) data to identify equilibrated and disequilibrated fluid gradients. Fields from Paleozoic and Mesozoic basins in the Arabian Peninsula at different stages of delineation, appraisal, development and management are discussed as examples to demonstrate the role of fluid characterization in aiding the evaluation of top, lateral and fault seal integrity, and in providing insights into the sealing and buffering effects of reservoir heterogeneity on trapping mechanism, fluid distribution and flow capacity. Examples discussed include (1) reservoir heterogeneity controlling fluid distribution and trapping mechanisms in two neighbouring gas fields, (2) geochemical evidence for a lateral seal separating gas condensate region from oil discovered during field development, (3) solid reservoir bitumen atop a giant gas field, (4) selective gas leakage through anhydrite seal, and (5) geochemical evidence for fault-controlled reservoir compartmentalization rather than a hydrodynamically tilted fluid contact in a field at an appraisal stage where PVT data are limited or inconclusive. Put in structural context, seal-related interpretations of the fluid data are aligned with observations from seismic and geological data on the existence of faults with favourable orientations or facies changes. Thematic collection: This article is part of the Fault and top seals 2022 collection available at: https://www.lyellcollection.org/topic/collections/fault-and-top-seals-2022

Study on anisotropic effect correction term of Virial–Peng–Long equation of state

In order to correct the calculation deviation of the VPL (Virial–Peng–Long) equation of state (EOS) of gaseous detonation products, based on the L-J (Lennard-Jones) 6-12 spherical molecular potential, the change in the pressure–volume–temperature (PVT) relationship of polyatomic molecular gas at high temperature and high pressure due to the anisotropic effect of the molecular structure is calculated, further improving the accuracy of the VPL EOS in describing the PVT relationship of high temperature and high pressure gas. In this paper, on the basis of the original VPL EOS, the anisotropic effect correction term, whose form is similar to the virial terms, is introduced. Taking the PVT data of CH4 (P = 0.02–187.3 GPa and T = 752–4369 K) as the reference value, the multi-objective optimization genetic algorithm is used to calibrate the coefficients of the correction term, and the VPLR (Virial–Peng–Long-Refined) EOS is established. In order to further analyze and evaluate the improvement in the accuracy of the VPLR EOS, the PVT relationship of O2 (P = 0.01–111.5 GPa and T = 573–4066 K), CO2 (P = 0.42–111.1 GPa, T = 578–5060 K), and CO (P = 0.1–130.2 GPa, T = 591–4286 K) in a wide high temperature and high pressure region was calculated and compared with the calculated values of the original VPL EOS, Virial–Han–Long EOS, and Virial–Wu EOS. The results show that the global accuracy of the VPLR EOS has been significantly improved compared with the other three virial EOSs and that the relative deviation between the calculation results and the reference values can converge in a smaller range at most pressures and temperatures.

Production Step-Up of an Oil Well through Nodal Analysis

A nodal analysis technique is used in this paper to optimize the production of oil in the well “X-3” located in the Rio del Rey basin. Several wells in that basin are facing a low productivity problem. That problem represents the key problem of this paper. The goals of this paper are to quantify the effects of four parameters (tubing size, flowline size, wellhead pressure, and skin) on production; to determine the causes of abnormally reduced productivity; and to choose optimal values to enhance the well flow rate. To achieve these goals, it is necessary to use well pressure volume temperature (PVT) and reservoir data and the nodal analysis technique. The nodal analysis method is applied at two main node points (wellbore and wellhead), where inflow and outflow performances are carefully reviewed with sensitivity analysis. The two nodal points are selected because of the high-pressure drops they observe. The analysis results showed that the selection of some wrong equipment diameters (production tubing and surface flowline) and values (wellhead pressure and skin) strongly influenced (badly) the flow rate. In fact, the increase in tubing and flowline diameters positively influenced the flow velocity, while the diminution in the wellhead pressure increased the flow rate as well. The production of the well increased from about 800 to 1000 barrels per day. Although nodal analysis remains an effective method to determine the causes of low productivity, it is limited compared to transient tests and production logging.

Laboratory and Theoretical investigations of Petroleum Reservoir Fluid Properties

Understanding the Pressure-Volume-Temperature (PVT) properties is very important to many kinds of petroleum determinations like calculations of reservoir fluid properties; expect the future performance, selection of enhanced oil recovery methods, and for production facilities design. Models for expecting reservoir fluid properties has been increased attention during last decade by knowing reservoir pressure and temperature, oil API gravity, and gas gravity. In general, PVT properties are obtained from laboratory experiments but in some cases In this study, complete PVT lab experiments were done and evaluated the most frequently used empirical black oil PVT correlations for application in the Middle East. Empirical PVT Correlations for the Middle East crude oil was compared as a function of commonly available PVT data. Correlations compared for: Bubble point pressure, solution gas oil ratio, oil formation volume factor, oil density, and oil viscosity. After evaluating the Empirical correlations, the crude sample was characterized using different EOS to arrive at one EOS model that accurately describes the PVT behavior of crude oil produced. The multi-sample characterization method is used to arrive at one consistent model for crude oil for the whole reservoir. The fluid sample is first analyzed for consistency to ensure that they are representative of oil produced, then it is used to obtain parameters for EOS model. The tuning procedure for the EOS is done systematically by matching the volumetric and phase behavior results with laboratory results. Results showed that some correlations give good results in PVT properties compared to the laboratory and can be used with Libyan oil, while some give a high percentage of error.

Probabilistic Approach Guides Field Development Plan in Niger Delta

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207146, “Conceptual Field Development Plan for X Field,” by Khadijah Ibrahim, SPE, Petrus Nzerem, and Ayuba Salihu, SPE, Nile University of Nigeria, et al. The paper has not been peer reviewed. _ The development plan of a new oil field discovered in a remote offshore environment in the Niger Delta was evaluated. Because the oil in place was uncertain, a probabilistic approach was used to estimate the stock-tank oil originally in place (STOOIP) using low, base, and high cases. The base model is recommended for further implementation. The quantity of oil produced is estimated at 15,000 STB/D, which will require a separator that has the capacity of holding a liquid rate of approximately 20,000 STB/D. The authors find that this developmental plan is cost-effective. Introduction Field X is offshore Nigeria in water depths of 330 ft. The field has an uncertain oil in place, with a possibility of low, base, and high cases. The field reservoir information is available from the exploration and seismic well data provided. The field size (i.e., STOOIP) is estimated at 95 million (low), 145 million (base), and 300 million (high) STB, with an irreducible water saturation of 22%. The reservoir depth is 7,400 ft true vertical depth subsea (TVDSS), with a net pay thickness of 45 ft. The aquifer supporting the reservoir has a strength of 9, with a permeability of 200 md and a porosity of 18%. Generally, the reservoir has permeability and compressibility of the rock formation obtained from the log results of 250 md and 3.5×10–6 psi–1. The reservoir pressure is measured to be 3,400 psia. The field development plan will incorporate production forecasts from the target date of first production, 1 January 2023, through 1 January 2038, for a 15-year field life. The complete paper presents the field development strategies and economic analysis of different well models to evaluate the most-efficient modeling profile for the new field. Methodology Well-Modeling Profile and Performance Analysis. It is assumed that the field will be produced using a platform with a wellhead reference depth of 20 ft above the rotary kelly bushing. The well is assumed to be a vertical well with no deviation. The skin value is estimated to be 1, thereby having a slight effect on the productivity index. Pressure/volume/temperature (PVT) data were available for the oil in place. Using commercial software, the fluid parameters for bubblepoint pressure, oil volume factor, and oil viscosity were matched in the PVT analysis at a reservoir temperature of 170°F and a pressure of 3,400 psia. The measurements of formation volume factor, gas/oil ratio, and oil viscosity at this pressure were used to provide a single match point for the generation of PVT data.

Predicting In-Situ Physical Properties for Gas Condensates From Fluid Pressure Gradients

New hybrid EOS-PVT models have been developed for estimating in-situ fluid properties for retrograde gas condensate fluids. The hybrid models are based on tuned equation of state (EOS) properties for a globally distributed set of quality-controlled (QC) pressure-volume-temperature (PVT) analyses of gas condensates in which the condensate-to-gas ratio (CGR) ranges from ~5 to 350 bbl/MMscf and for which in-situ fluid densities range from ~0.2 to 0.6 g/cm3. The hybrid EOS-PVT models are based on the observation that in-situ fluid densities derived from measured fluid pressure gradients (FPG) are highly correlated with EOS-derived in-situ fluid densities obtained from tuned EOS models and QC PVT data. Using fluid density values calculated using in-situ FPG data, it is now possible to estimate a variety of gas condensate fluid properties such as viscosity, mole fraction of methane (XCH4), density, acoustic velocity, and adiabatic fluid moduli, the latter being a critical input for Gassmann fluid substitution models. The utility of the new hybrid EOS-PVT models lies in their ability to facilitate rapid evaluation of stranded gas resources without having to take a fluid sample for laboratory PVT analysis. Properly executed FPG data also have great utility as a reliable QC for laboratory-measured PVT properties. We present hybrid EOS-PVT models for CGR, viscosity, in-situ fluid density, and acoustic velocity for a broad suite of gas condensates. A significant contribution of this work is the development of a new fluid acoustic properties model that enables estimation of the adiabatic fluid modulus (Kad) for retrograde gas condensates. Using predictive models reported here for fluid density [ρ] and velocity [Vp], the adiabatic fluid modulus is easily calculated. The model reported for fluid velocity was validated with direct measurements of fluid acoustic velocity on a live sample, at in-situ conditions, from the HP/HT Shearwater Field in the Central North Sea (CNS).

Simulation and Modelling of Water Injection for Reservoir Pressure Maintenance

Water injection has shown to be one of the most successful, efficient, and cost-effective reservoir management strategies. By re-injecting treated and filtered water into reservoirs, this approach can help maintain reservoir pressure, increase hydrocarbon output, and reduce the environmental effect. The goal of this project is to create a water injection model utilizing Eclipse reservoir simulation software to better understand water injection methods for reservoir pressure maintenance. A basic reservoir model is utilized in this investigation. For simulation designs, the reservoir length, breadth, and thickness may be changed to different levels. The water-oil contact was discovered at 7000 feet, and the reservoir pressure was recorded at 3000 pounds per square inch at a depth of 6900 feet. The aquifer chosen was of the Fetkovich type and was linked to the reservoir in the j+ direction. The porosity was estimated to be varied, ranging from 9% to 16%. The residual oil saturation was set to 25% and the irreducible water saturation was set at 20%. The vertical permeability was set at 50 md as a constant. Pressure Volume Temperature (PVT) data was used to estimate the gas and water characteristics.

Chain-based machine learning for full PVT data prediction

Building machine learning (ML) models based on pressure-volume-temperature (PVT) data is of paramount importance to capture trends and predict fluid behavior in a very heterogeneous and highly nonlinear thermodynamic system. PVT samples stored in an oil company database are often not complete and might be missing properties; both black oil and compositional. Before delving into building optimized fluid models, it is required to have a clean and structured PVT database complete with all the required properties. We present multiple novel algorithms developed to accurately predict a complete set of black oil and compositional properties within a PVT database. The proposed methodology consists of predicting properties in series, starting from a minimal set of data (black oil and compositional) and obtaining a complete set of data for all PVT samples. The order through which this is completed relies on benefiting from the existing data to predict missing data starting from the highest correlating data to the lowest. We also honored the physical nature of correlations between properties, and consequently, ranked properties for prediction as this leads to less error propagation. In addition, we have implemented data clustering prior to training ML models. Clustering is used to categorize the fluid samples into families based on the collective behavior of their different features, and hence, improve the quality of the PVT samples' properties prediction using machine learning. Several options are tested where clustering is performed using black oil properties only, compositional properties, or a combined set of black oil and compositional properties and the clustering scenario leading to the least prediction error is adopted. We have trained ML models to generically predict all black oil and compositional properties resulting from laboratory experiments, including molecular weights (MW) and mole fractions of heavy fractions (any set of heavy fractions) from the mole fractions of all the commonly available components up to C7+ and molecular weight of C7+. The massive data set used in this paper enabled comprehensive testing of the developed algorithms and provided striking accuracy of the predicted PVT properties; especially the compositional ones. Despite all the significant efforts shown in the literature concerning predicting PVT properties, the missing link is a systematic methodology to complete PVT samples' properties in a consistent manner. Furthermore, the focus in the literature is mainly on forecasting black oil properties; compositional properties are scarcely considered. Algorithms developed in this paper address these two limitations and are tested using a uniquely large data set available for onshore and offshore fields and reservoirs in Abu Dhabi. No previous algorithms, to the best of our knowledge, are tested on such a large data set. • Systematic, chain-based, methodology to complete PVT samples' compositional and black oil properties in a consistent manner. • Clustering to categorize fluid samples based on their thermodynamic behavior and reveal deep insights of these categories. • Application of the proposed algorithms for compositional splitting was a powerful illustration of the proposed algorithms. • Algorithms are tested using a uniquely large data set available for onshore and offshore fields and reservoirs in Abu Dhabi.

Evaluating Precision of Annular Pressure Buildup (APB) Estimation Using Machine-Learning Tools

Summary Annular pressure buildup (APB) is caused by heating of the trapped drilling fluids (during production), which may lead to burst/collapse of the casing or axial ballooning, especially in subsea high-pressure/high-temperature wells. The objective of this paper is to apply machine-learning (ML) tools to increase precision of the APB estimation, and thereby improve the fluid and casing design for APB mitigation in a given well. The APB estimation methods in literature involve theoretical and computational tools that accommodate two separate effects: volumetric expansion [pressure/volume/temperature (PVT) response] of the annulus drilling fluids and circumferential expansion (and corresponding mechanical equilibrium) of the well casings. In the present work, ML algorithms were used to accurately model “fluid density = f(T, P)” based on the experimental PVT data of a given fluid at a range of (T, P) conditions. Sensitivity analysis was performed to demonstrate improvement in precision of APB estimation (for different subsea well scenarios using different fluids) using the ML-based models. This study demonstrates that, in several subsea scenarios, a relatively small error in the experimental fluid PVT data can lead to significant variation in APB estimation. The ML-based models for “density = f(T, P)” for the fluids ensure that the cumulative error during the modeling process is minimized. The use of certain ML-based density models was shown to improve the precision of APB estimation by several hundred psi. This advantage of the ML-based density models could be used to improve the safety factors for APB mitigation, and accordingly, the work may be used to better handle the APB issue in the subsea high-pressure/high-temperature wells.

Optimizing Water Injection Performance by Using Sector Modeling of the Mishrif Formation in West Qurna-1 Oil Field, Southern Iraq

The regular job of a reservoir engineer is to put a development plan to increase hydrocarbon production as possible and within economic and technical considerations. The development strategy for the giant reservoir is a complex and challenging task through the decision-making analysis process. Due to the limited surface water treatment facility, the reservoir management team focuses on minimizing water cut as low as possible by check the flow of formation and injected water movement through the Mishrif reservoir. In this research, a representative sector was used to make the review of water injection configuration, which is considered an efficient tool to make study in a particular area of the entire field when compared with the full-field model on the basis of time-consuming and computational analysis. The sector model was neighboring by extra grid blocks and three pseudo wells as injector wells to realize the pressure on the sector boundary, which attained an acceptable history matching. The fluid model and physics model were introduced by using Pressure Volume Temperature data of well involved in the study area and two relative permeability curves. Fourteen wells were utilized in this work, four wells are injectors, and the rest are producer. The development scenarios were implemented by setting various targets of oil production and different water injection rates required for pressure maintenance operations. Optimization of water cut has been applied by adjustment of production and injection rates and shut off the high water cut intervals. The results obtained from this study showed that the inverted 9-spot has a good recovery which is illustrated in the case_2C, the production rate was (49,000 STB/D) with minimum water cut (27.5%) as compared with a five-spot pattern.

Pressure-volume-temperature relationships of diisononyl phthalate and phase behavior of carbon dioxide/diisononyl phthalate binary system

The pressure-volume-temperature (PVT) relationships of pure diisononyl phthalate (DINP) were measured at temperatures of 303.15–363.15 K and pressures up to 160 MPa by a variable-volume method. Using a synthetic method that involved observing the bubble point, the phase behavior of the CO2/DINP binary system was also measured. The phase boundaries were determined at temperatures of 313.15–393.15 K and CO2 molar fractions of 0.3–0.9. The characteristic parameters of DINP were obtained by correlating the measured PVT data using the Sanchez-Lacombe equation of state (SL EoS), and the bubble points of the system were correlated using the SL EoS with the determined characteristic parameters. The interaction parameter exhibited temperature dependence, and the introduction of the temperature dependence improved the correlation.

A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble

Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the development cycle of an oil field. Bubble point pressure is required in many petroleum engineering calculations such as reserves estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. Additionally, bubble point pressure is a key input parameter in most oil property correlations. Thus, an error in a bubble point pressure estimate will definitely propagate additional error in the prediction of other oil properties. Accordingly, many bubble point pressure correlations have been developed in the literature. However, they often lack accuracy, especially when applied for global crude oil data, due to the fact that they are either developed using a limited range of independent variables or developed for a specific geographic location (i.e., specific crude oil composition). This research presents a utilization of the state-of-the-art Bayesian optimized Least Square Gradient Boosting Ensemble (LS-Boost) to predict bubble pointpressure as a function of readily available field data. The proposed model was trained on a global crude oil database which contains (4800) experimentally measured, Pressure–Volume–Temperature (PVT) data sets of a diverse collection of crude oil mixtures from different oil fields in the NorthSea, Africa, Asia, Middle East, and South and North America. Furthermore, an independent (775) PVT data set, which was collected from open literature, was used to investigate the effectiveness of the proposed model to predict the bubble point pressure from data that were not used during the model development process. The accuracy of the proposed model was compared to several published correlations (13 in total for both parametric and non-parametric models) as well as two other machine learning techniques, Multi-Layer Perceptron Neural Networks (MPL-ANN) and Support Vector Machines (SVM). The proposed LS-Boost model showed superior performance andremarkably outperformed all bubble point pressure models considered in this study.

The glass transition of amorphous polymers: About a continuous equation of state and its derivatives

AbstractDescribing the pressure‐volume‐temperature (PVT) behavior of polymers is often hindered due to limitations of models and theories. In this work, we propose the logistic function to derive a phenomenological equation of state (EOS) that overcomes these limitations. Without dividing the temperature domain into several intervals, this continuous EOS describes the PVT behavior of amorphous polymers in the equilibrium state, the vitreous state and at glass transition. Discontinuities at glass transition are inherently eliminated. The EOS is continuously differentiable with respect to temperature and pressure. The thermal expansion coefficient and compression modulus thus can be determined at any valid reference state. The EOS linearly depends on temperature both in the equilibrium state and vitreous state. Furthermore, a parameter of the EOS is interpreted as the pressure‐dependent glass transition temperature. Using PVT data of polyethersulfone (PES), measured in the temperature range of 20 − 360 °C and pressure range of 10 − 200 MPa in both the isobaric and standard isothermal mode, the validity of the EOS is discussed. Polymer processing simulation and solid mechanics benefit from the EOS and its derivatives, respectively. In addition, the EOS can be used to determine the glass transition temperature from PVT data.

Mud-Gas Breakthrough Equinor Develops Real-Time Reservoir-Fluid Identification

For all that logging-while-drilling has provided since its wide-spread adoption in the 1980s, there is one thing on the industry’s wish list that it could never offer: an accurate way to tell the difference between oil and gas. A new technology created by petrotechnicals at Equinor, however, has made this possible. The innovation could be thought of as a pseudo-log, but Equinor is describing it as a reservoir-fluid-identification system. Using an internally developed machine-learning model, it compares a database of more than 4,000 reservoir samples against the real-time analysis of the mud gas that flows up a well as it is drilled. Crunched out of the technology’s various hardware and software components is a prediction on the gas/oil ratio (GOR) that the rock being drilled through will have once it is producing. Since this happens in real time, it boils down to an alert system for when drillers are tapping into uneconomic pay zones. “This is something people have tried to do for 30 years - using partial information to predict entire oil and gas properties,” said Tao Yang. He added that “the data acquisition is rather cheap compared with all the downhole tools, and it doesn’t cost you rig time,” highlighting that the mud-gas analyzer critical to the process sits on a rig or platform without interfering with drilling operations. Yang is a reservoir technology specialist at Equinor and one of the authors of a technical paper (SPE 201323) about the new digital technology that was presented at the SPE Annual Technical Conference and Exhibition in October. He and his colleagues spent more than 3 years building the system which began in the Norwegian oil company’s Houston office as a project to improve pressure/volume/temperature (PVT) analysis in tight-oil wells in North America. It has since found a home in the company’s much larger offshore business unit in Stavanger. Offshore projects designed around certain oil-production targets can face harsh realities when they end up producing more associated gas than expected. It is the difference between drilling an underperforming well full of headaches and one that will pay out hundreds of millions of dollars over its lifetime. By introducing real-time fluid identification, Equinor is trying to enforce a new control on that risk by giving drillers the information they need to pull the bit back and start drilling a side-track deeper into the formation where the odds are better of finding higher proportions of oil or condensates. At the conference, Yang shared details about some of the first field implementations, saying that in most cases the GOR predictions made by the fluid-identification system were confirmed by traditional PVT analysis from the trial wells. Unlike other advancements made on this front, he also said the new approach is the first of its kind to combine such a large database of PVT data with a machine-learning model “that is common to any well.” That means “we do not need to know where this well is located” to make a GOR prediction, said Yang.

A semi-empirical method for predicting GOC in oil reservoirs with limited PVT dataset

Depending on the type, quality and quantity of available data, different methods exist to determine the hydrocarbon–hydrocarbon contacts in reservoirs. These methods are either direct or indirect. While the direct method involves consciously drilling a well to penetrate potential fluid contacts, the indirect methods include pressure measurements across at least two different fluid types, and/or, the acquisition and subsequent characterization of downhole fluid samples. Recognizing that most petroleum reservoirs are compositionally graded to varying degrees, compositional grading simulation (CGS) is an inexpensive indirect method of evaluating potential gas–oil contact (GOC) in petroleum reservoirs. However, this typically requires detailed pressure–volume–temperature (PVT) data of the hydrocarbon fluids. But, in many cases, PVT data may be limited. To address the challenges in such cases, this paper proposes a new predictive model and practical workflow, hence extending the applicability of CGS. Employing several PVT datasets and rigorous CGS for known black and volatile oil reservoirs, a new semiempirical function is developed for estimating saturation pressure gradient from gas oil ratio (GOR), which is a readily measurable fluid property. Complementing this function, is the identification of a robust empirical model to predict saturation pressure. The associated workflow provides a systematic integration of the predictive saturation gradient and saturation pressure models to estimate the degree of undersaturation, and hence, potential GOC depth in a reservoir characterized by limited PVT dataset. For validation tests, some cases of saturated and undersaturated reservoirs in the Niger Delta are presented. The results confirm the applicability of the new method in the Niger Delta and elsewhere.

Thermoelastic Properties of K0.7Na0.3AlSi3O8 Hollandite and NaAlSi2O6 Jadeite: Implication for the Fate of the Subducted Continental Crust in the Deep Mantle

The thermoelastic properties of K0.7Na0.3AlSi3O8 hollandite and NaAlSi2O6 jadeite, synthesized from a (K, Na)-felspar (microcline), were investigated by a combination of in situ energy dispersive synchrotron X-ray radiation and multi-anvil techniques at high pressure (P) and temperature (T) up to 21 GPa and 1700 K. The second-order phase transformation was found to occur in hollandite at ~16 GPa from tetragonal I/4m (hollandite-I) to monoclinic I2/m (hollandite-II), which confirms the previous report that the incorporation of Na in the hollandite structure decreases the transformation pressure. Fitting the pressure–volume–temperature data to the Birch–Murnaghan equation of state yielded estimates of the thermoelastic parameters for jadeite as well as the K0.7Na0.3AlSi3O8 hollandite-I and -II phases, which indicate that the incorporation of Na is likely to decrease the bulk moduli of both hollandite phases. The obtained thermoelastic parameters were combined with those of other mantle minerals reported previously to estimate the density of continental materials along an average mantle geotherm. Based on our results, continental crust and sediment become, respectively, 11% and 15% denser than the pyrolitic mantle at pressure >10 GPa, suggesting that once pulled down to the critical depth of ~300 km, the continental portions of the slab can subduct further into the deep mantle, down to the lowermost part of the mantle transition region.

PARTICLE SWARM OPTIMIZATION OF SOLUTION GAS OIL RATIO

Reservoir fluid properties are very important in reservoir engineering computations such as material balance calculation, well test analysis, reserves estimate and numerical reservoir simulations. Ideally, these properties should be obtained from laboratory pressure-volume-temperature (PVT) analysis. Quite often, however, these measurements are either not available, or very costly to obtain. In such cases, empirically derived correlations are used to estimate the needed properties, all computation therefore, will depend on the accuracy of the correlations used for estimating the fluid properties. Hence in this study, Standing’s correlation for estimating the solution gas-oil ratio was optimized using a particle swarm optimization (PSO) algorithm to minimize the error associated in estimating solution gas-oil ratio from correlation at various depletion pressure. The optimized correlation was taken as a function of bubble point pressure, API gravity, gas gravity and reservoir temperature. PVT data from differential liberation test was used to validate this study’s correlation and the result obtained shows that the optimized correlation for this study matches closely with the experimental values, also the newly optimized correlation was validated with other models and the results gave the least average relative error of 3.34 and a correlation coefficient of 0.998 after 216th successive iterations by the particle swarm optimization algorithm.
 Okotie, S. | Department of Petroleum Engineering, Federal University of Petroleum Resources (FUPRE), Effurun, Delta State, Nigeria

Effect of Free-Volume Hole Fraction on Dynamic Mechanical Properties of Epoxy Resins Investigated by Pressure–Volume–Temperature Technique

Dynamic mechanical analysis experiments were carried out to investigate the mechanical properties of four types of chemically different epoxy resins. Pressure-volume-temperature (PVT) experiments were performed to determine the free-volume hole fraction (hPVT) of each epoxy resin using the Simha-Somcynsky lattice-hole theory. Using the Williams-Landel-Ferry equation, the correlations between the relative hole fraction (1 - hPVTTr/hPVT, where hPVTTr is the hole fraction at a reference temperature Tr) and four typical parameters reflecting dynamic mechanical properties [storage modulus (E'), loss modulus (E″), damping factor (tanδ), and complex viscosity (|η*|)] were studied in the temperature range from Tg(PVT) (the glass transition temperature determined by PVT data) to Tg(PVT) + 100 °C. In the temperature range from Tg(Eonset') (temperature corresponding to the intersection of the two tangent fitting lines in the E'(T) curve indicating the glassy-state and glass-transition stages) to Tg(PVT) + 100 °C, the variations in the four dynamic mechanical parameters with a relative hole fraction could be separated into two distinct categories: (i) log[E'(T)] and log[|η*|(T)] decreased linearly to their minimum values and then remained nearly unchanged with increasing relative hole fraction, and (ii) log[E″(T)] and log[tanδ(T)] first increased monotonically to their maximum values and then decreased linearly with the increasing relative hole fraction. This study demonstrates that the PVT technique is a feasible and reliable experimental method to determine the hole fractions of thermoset polymers.

Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Pressure–Volume–Temperature (PVT) characterization of a crude oil involves establishing its bubble point pressure, which is the pressure at which the first gas bubble forms on a fluid sample while reducing pressure at a stabilized temperature. Although accurate measurement can be made experimentally, such experiments are expensive and time-consuming. Consequently, applying reliable artificial intelligence (AI)/machine learning methods to provide an accurate mathematical prediction of an oil’s bubble point pressure from more easily measured characteristics can provide valuable cost and time savings. This paper develops and compares four neurocomputing models applying algorithms consisting of a Multilayer Perceptron (MLP), a Radial Basis Function trained with a Genetic Algorithm (RBF-GA), a Combined Hybrid Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System (CHPSO-ANFIS), and Least Squared Support Vector Machine (LSSVM) tuned with a coupled simulated annealing (CSA) optimizer. Based on a comprehensive analysis, although the four proposed models yield acceptable outputs, the CHPSO-ANFIS model has the best performance with the average absolute relative deviation of 0.846, the standard deviation of 0.0126, the root mean square error of 43.21, and the correlation coefficient of 0.9902. These algorithms are deployed for the accurate estimation of the bubble point pressure from the giant Ahvaz oil field (Iran).