Pressure Volume Temperature Research Articles

Water density–pressure–temperature modeling for drilling fluids in real-time HPHT applications

Accurate equations for extrapolating the density of water to varying pressures and temperatures are important for the petroleum industry. Water is a key component in drilling fluids, and extrapolation models of water are essential for calculating the density of the fluid as a whole for different depths in the well. This is further of paramount importance to be able to keep the pressure in the well within safety limits. This paper shows and explains how density–pressure–temperature (DPT) models referred to in the field of Petroleum are not well-suited for high-pressure, high-temperature (HPHT) wells which are becoming more and more common. It also discusses DPT- and pressure–volume–temperature (PVT) models from other disciplines and demonstrates that models with the accuracy needed for HPHT wells, are excessively complex and have unnecessary long execution times. For many challenging wells, hydraulic models run today in parallel with the operation in real-time and may constitute one of several components in a digital twin. In such applications it is important that each component runs as efficiently as possible, such that the entire system is able to deliver its results in time. In this work, new polynomial DPT models are constructed, that fill this gap in water DPT modeling. I.e., they are designed for being used for calculating the water density in real-time, and cover HPHT wells with pressure up to 200MPa and temperature up to 260°C. It is demonstrated that it is possible to construct polynomial models that are 1−2 decades faster than the industry standard used for comparison, while also being more accurate over the pressure and temperature range relevant to HPHT wells. If the accuracy can be slightly relaxed, a new model that is 2−3 orders of magnitude faster than the industry standard is proposed.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.

Modelling across Multiple Scales to Design Biopolymer Membranes for Sustainable Gas Separations: 2-Multiscale Approach

The majority of materials used for membrane-based separation of gas mixtures are non-renewable and non-biodegradable, and the assessment of alternative bio-based polymers requires expensive and time-consuming experimental campaigns. This effort can be reduced by adopting suitable modelling approaches. In this series of works, we propose various modelling approaches to assess the CO2/CH4 separation performance of eight different copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV) using a limited amount of experimental data for model calibration. In part 1, we adopted a fully atomistic approach based on Molecular Dynamics (MD), while, in this work, we propose a multiscale methodology where a molecular description of the polymers is bridged to a macroscopic prediction of its gas sorption behaviour. PHBV structures were simulated using MD to obtain pressure–volume–temperature data, which were used to parametrise the Sanchez–Lacombe Equation of State. This, in turn, allows for the evaluation of the CO2 and CH4 solubility in the copolymers at various pressures and compositions with little computational effort, enabling the estimate of the sorption-based selectivity. The gas separation performance obtained with this multiscale technique was compared to results obtained with a fully atomistic model and experimental data. The solubility–selectivity for the CO2/CH4 mixture is in reasonable agreement between the two models and the experimental data. The multiscale method presented is a time-efficient alternative to fully atomistic methods and detailed experimental campaigns and can accelerate the introduction of renewable materials in different applications.

تحسين علاقة المرهون لضغط نقطة الفقاعة للنفط الخام الليبي

Accurate estimation of crude oil bubble point pressure plays a vital role in many petroleum engineering calculations, such as reserve estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. The Pb can be measured in the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. In this paper, the Al-Marhoun correlation for bubble point pressure for black oil reservoirs is improved using the linearization of non-linear regression techniques to fit Libyan crudes accurately. A total of 62 PVT data reports taken from various Libyan oil fields were used in the study. The PVT data consist of an oil gravity range of 24.7 ºAPI to 46.8 ºAPI and bubble point pressures of 123 psig to 6100 psig. Statistical error analyses and graphical methods were used to evaluate the original and modified Al-Marhoun correlations. The results showed that the improved Al-Marhoun correlation exhibits significantly lower average absolute error and deviation than the published ones. The correlation coefficient of R2 of the improved Al-Marhoun correlation is 96.70%, and the average percent error (APE) is 0.70% with a standard deviation (SD) of 14.70.

Evaluating the impact of sodium chloride and iron carbonate ions on gas hydrate formation in Monoethylene Glycol‐enhanced aqueous solutions

AbstractThe management and prevention of hydrates are crucial for the gas industry. This study delves into the intricate challenges associated with gas hydrate formation, with a specific focus on investigating the impact of corrosion by‐products on prevention strategies. Employing a distinctive methodology, the sapphire pressure–volume temperature (PVT) cell was utilized. Experimental tests were conducted using sodium chloride (NaCl) concentrations of 1% and 3% to simulate brine solution levels at the wellhead, incorporating 3% filtrate and unfiltered iron carbonate (FeCO3) as corrosion products associated with the production process. The 1% and 3% salt concentrations were chosen to encompass a broad range of temperature depressions, reflecting common industry standards for simulating realistic environmental conditions. PVT cell test conditions ranged from 80 to 200 bar, with increments of 40 bar. The experiments investigate the effects of common pipeline salts on a monoethylene glycol (MEG)/water mixture in the presence of methane gas at typical industry high‐pressure conditions. The investigation uncovers that the introduction of salts to water, methane, and MEG solutions serves as a hydrate inhibitor, with inhibitory effects directly correlated to salt concentration. While generally hydrate growth inhibition is beneficial in natural gas pipelines, the findings indicate that elevated salt concentrations and lower pressure conditions contribute to the formation of larger hydrates, heightening the risk of surface adhesion and potentially introducing complications in piping equipment, despite the decreased temperature at which these hydrates form due to the inhibitory effects of the salts. In particular, the mixed condition of 3% NaCl and 3% FeCO3 (filtered) has the strongest effect. Examination of hydrate formation temperature and macroscopic observations suggests that the existence of substantial precipitates, as evidenced in the unfiltered FeCO3 sapphire cell experiment, may have the potential to enhance hydrate growth.

Estimation of antigorite wave velocities in subduction conditions based on first-principles thermoelasticity

The most abundant serpentine mineral in subduction settings, antigorite has one of the highest water storage capacities and is involved in seismicity. Seismic wave velocities of antigorite are important for detecting and quantifying serpentinization within the mantle wedge and the subducting oceanic plate. At present, the elastic properties of antigorite at high pressures and temperatures are unclear. In this study, we have investigated pressure-volume-temperature (P-V-T) data and thermodynamic properties of antigorite using first-principles molecular dynamics (FPMD) simulations. Using these simulations results, we computed the relevant thermoelastic parameters and estimated compressional and shear wave velocities (vP and vS) of antigorite in subduction conditions. A simplified velocity model of antigorite with its coexisting mantle anhydrous phases was introduced to help us understand the potential effect of serpentinization on the seismic velocity of mantle rocks. Combined with seismic observations, we re-evaluated some velocity anomalies within forearc mantle wedges and established reliable serpentinization budgets. These results can provide preliminary evaluations and reliable constraints on serpentinization and water content in mantle rocks, which has important implications for understanding global plate dynamics and the deep water cycle.

All-optical method to directly measure the pressure–volume–temperature equation of state of fluids in the diamond anvil cell

We have developed a new all-optical method to directly measure the pressure–volume–temperature (PVT) equation of state (EOS) of fluids and transparent solids in the diamond anvil high pressure cell by measuring the volume of the sample chamber. Our method combines confocal microscopy and white light interference with a new analysis method, which exploits the mutual dependence of sample density and refractive index: Experimentally, the refractive index determines the measured sample chamber thickness (and therefore the measured sample volume/density), yet the sample density is by far the dominant factor in determining the variation in the refractive index with pressure. Our analysis method allows us to obtain a set of values for the density and refractive index, which are mutually consistent and agree with the experimental data within error. We have conducted proof-of-concept experiments on a variety of samples (H2O, CH4, C2H6, C3H8, KCl, and NaCl) at ambient temperature and at high temperatures up to just above 500 K. Our proof-of-concept data demonstrate that our method is able to reproduce known fluid and solid EOS within error. Furthermore, we demonstrate that our method allows us to directly and routinely measure the PVT EOS of simple fluids at GPa pressures up to, at least, 514 K (the highest temperature reached in our study). A reasonable estimation of the known sources of error in our volume determinations indicates that the error is currently ±2.7% at high temperature and that it is feasible to reduce it to ca. ±1% in future work.

40Ar diffusion in phlogopite: [formula omitted] atomistic calibration and implications for Ar–melt–solid kinetic interactions and ascent dynamics of mantle xenoliths and kimberlites

40Ar/39Ar ages of phlogopite from kimberlite-hosted mantle xenoliths are commonly older than the kimberlite eruption, despite the fact that argon is supposed not to be retained by phlogopite at temperatures above hydrothermally-derived Ar closure temperatures (<500°C). Combining Molecular Dynamics (MD) with Nudged Elastic Band (NEB) and Transition State Theory (TST), we investigate 40Ar−PVT (Pressure−Volume−Temperature) relationships in pristine, defect-free, phlogopite and show that 40Ar diffusivity is several orders of magnitude slower than existing estimates with a strong effect of pressure on diffusion rates and retention of 40Ar at mantle conditions. These results imply to fundamentally revise residence- and transit-time estimates based on Ar kinetics in phlogopite assuming simple diffusive relaxation during upwelling. When accounting for pressure, 40Ar retention trends in phlogopite predict substantially slower kimberlite ascent rates than documented by independent chronometers, indicating that 40Ar resetting during ascent in phlogopite does not result from simple decompression-driven diffusive relaxation. We argue that 40Ar remobilization probably involves secondary structural–textural modifications induced by reaction-driven recrystallization or rim overgrowth. These findings have far-reaching consequences in terms of argon isotopic mobility at mantle depths as well as for dating and tracing metasomatic events during crust−mantle interactions in the evolution of the subcontinental mantle.

Study Reviews Reservoir Engineering Aspects of Geologic Hydrogen Storage

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23943, “Reservoir Engineering Aspects of Geologic Hydrogen Storage,” by Johannes F. Bauer, Mohd M. Amro, SPE, and Taofik Nassan, SPE, Technical University Bergakademie Freiberg, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. Reproduced by permission. _ Safe and effective large-scale storage of hydrogen (H2) is one of the greatest challenges of the global energy transition and can be realized only through storage in geological formations. The aim of the study detailed in the complete paper is to address and discuss the reservoir engineering aspects of geological H2 storage (GHS). The study is based on two sources: first, a comprehensive literature review and, second, experimental and numerical work performed by the authors’ institute. H2 Pressure/Volume/Temperature (PVT)/Phase Behavior The definition of the PVT/phase behavior of reservoir fluids is crucial in GHS because thermodynamic properties significantly affect safety and effectiveness. The properties of H2 are widely known and modeled, but its reaction with other gases, such as in-situ gases like natural gas in depleted gas reservoirs (DGR), currently is under investigation. Although the ideal gas law can account for H2 behavior at low pressure, accurate depiction of its thermodynamic properties requires more-sophisticated equations of state (EOS), especially when it is mixed with other gases such as methane. Most commercial reservoir simulators use EOS packages that can model the complex properties of these mixtures, mostly within required reliability. In most instances, calibration is still required if experimental PVT data are available. Reservoir Engineering of GHS. GHS projects must meet three crucial technical benchmarks for underground storage: capacity (storage volume), injectivity/productivity (rate of injection/withdrawal in relation to wellhead pressure), and containment integrity (prevention of leakage). Economic sustainability requires that projects must adhere to these standards, which may vary according to the selected geological formations. Although various subsurface structures can store H2, only specific formations such as salt caverns (SCs), saline aquifers (SAs), and depleted gas/oil reservoirs (DGRs and DORs), fulfill the requirements. While the complete paper discusses all three of these formation types in detail, this synopsis will concentrate on SCs. GHS in SCs. SC storage typically involves up to three wells for one cavern with a volume of up to 500,000 std m3, providing a delivery rate of 8,500–17,000 std m3/day. Working gas accounts for up to 65% of the total gas, while water should be kept to a minimum. SCs usually allow between six and 12 operating cycles per year, each lasting approximately 10 days for withdrawal periods. SCs offer high H2 purity and sealed storage. The storage capacity of caverns in Germany is determined by their volume and pressure limitations while avoiding any negative geomechanical effects. These caverns can range in depth from 500 to 2000 m, with heights of up to 400 m. Approximately 30 to 50% of the total stored volume is used as cushion gas to maintain production pressure. Estimating the capacity of these caverns relies on their geometry and thermodynamics, resulting in relatively lower uncertainties when compared with storages in porous media.

Volatile light hydrocarbons as thermal and alteration indicators in oil and gas fields

Volatile light hydrocarbons (VLH) are an essential component of reservoir petroleum fluids. Understanding of their origin and fate is crucial not only in exploration but increasingly also in petroleum engineering, as this greatly impacts fluid typing, proper mapping, recoverability and economic value. Due to their sensitivity to subsurface thermal stress and geological alteration processes, their proper characterisation holds promise to understanding the thermal conditions under which petroleum fluids were generated and subsequent fluid modifications during migration and within the reservoir. To study the behaviour of these hydrocarbons under different geological conditions we selected oil and gas fields from two giant conventional petroleum systems in the Arabian Peninsula collectively spanning the entire petroleum spectrum from heavy oil to dry and sour gas. In situ representative bottomhole or recombined pressure–volume–temperature (PVT) fluid composition data were constrained with molecular and stable carbon isotope geochemistry in key wells. Systematic covariance among the slope factor (SF) of propane to pentane and the isomer ratios of butane and pentane with reservoir engineering and geochemical variables in well-constrained black oil to gas condensate petroleum systems allowed the derivation of three formulas to calculate thermal maturity in terms of vitrinite reflectance equivalent from VLH fluid composition: (1) %VRe(SF) = 0.38 SF + 0.41, (2) %VRe(i4) = 1.70 (iC4/nC4) + 0.61, and (3) %VRe(i5) = 0.89 (iC5/nC5) + 0.56. The slope factor, iC4/nC4, and iC5/nC5 ratios all increase monotonically with the thermal evolution of unaltered fluids, allowing for effective application of their derived %VRe formulas across the entire unaltered fluid spectrum, from heavy oil to dry gas. Deviations from indigenous-fluid trends do occur for fluids altered by phase separation, biodegradation, thermal cracking, and thermochemical sulfate reduction (TSR), but corrections can be made to minimize uncertainty in assessing true thermal maturity of altered fluids while respecting other reservoir fluid properties such as gas-to-oil ratio (GOR) and saturation pressure relationships. For instance, although a single charge that has been phase fractionated yields fluids with variable GORs, saturation pressures and slope factors, their butane and pentane isomer ratios remain reflective of the original fluid maturity. In contrast, biodegradation-induced overestimation of maturity based on the isomer ratios of butane and pentane can be corrected by the less affected SF-derived maturity parameter. Reversal to lower apparent SF-derived maturity in thermally and TSR cracked fluids can, on the other hand, be corrected by considering the less affected butane and pentane isomer ratios. Overall, maturities calculated using VLH composition correspond well with fluid type defined based on phase behaviour and source-rock kinetics, thereby putting forward new tools to quantify thermal maturity of reservoir fluids that may be applicable in other petroleum systems.

Research on the State Equation of High-Pressure Expansion Process of Liquid Hydrogen

Liquid hydrogen, characterized by high purity, efficient storage and transportation, and renewable clean energy, has become a crucial component in the efficient utilization of energy. A profound understanding of the thermodynamic properties of the high-pressure expansion process of liquid hydrogen plays a significant role in the utilization of hydrogen energy, the development of liquid hydrogen booster pumps, and the implementation of liquid hydrogen-related projects. In this paper, starting from the perspective of the state equation, we conduct research on the large-scale high-pressure expansion process of liquid hydrogen. Several commonly used state equations are employed to calculate the PVT (Pressure-Volume-Temperature) values of this process, and appropriate modifications and combinations are made to improve the accuracy of the state equations. The advantages and disadvantages of the different equations of state for each pressure range and the causes of errors are further discussed based on the standard values given in the NIST database, and some suggestions are made for the improvement of the equations of state and the design of liquid hydrogen-related equipment.

A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects

This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

Fan-out panel-level package warpage and reliability analyses considering the fabrication process

With the continuous advancement of IC packaging technology, 3D and 2.5D packaging systems have emerged as the prevailing trends. The advantages of large area and high I/O density have made fan-out panel-level packaging (FOPLP) been considered as one of the best package types for 3D and 2.5D packaging applications. The discussions about FOPLP have been popular recently. Warpage and reliability of FOPLP are the main concerns of packaging engineers. However, it was found that warpage and reliability of FOPLP are strongly related to the fabrication process but not many research works have been published. Thus, reliability analysis procedure considering the residual stress of fabrication process was proposed in this paper to observe warpage and stress distribution during fabrication process. Then, this research procedure facilitates subsequent fatigue life calculations and predicts reliability of the package.First, the thermal stresses and warpage of the redistribution layer-first (RDL-first) process were conducted for an entire panel. Next, warpage and thermal stress analyses were conducted for a strip encapsulated with an epoxy molding compound (EMC) during compression molding. Then, a reliability analysis was conducted with an IC unit and incorporated the consideration of the residual stress induced during the fabrication processes.The compression molding analysis utilized the pressure–volume–temperature–cure (P–V–T–C) model to account for both the volume shrinkage caused by a thermal mismatch and the chemical shrinkage. Furthermore, dual shift factor model was used to model the viscoelastic behavior of EMC in this study during PMC analysis effectively.An observation of the stress distribution for an IC unit revealed that the maximum stress position was at the solder ball near the periphery, with a stress value of 147.309 MPa. The fatigue life calculated using the modified Coffin–Manson fatigue model was 417 cycles.

Equinor Leads Project To Turn Ubiquitous Drill Cuttings Into Precious PVT Samples

They were told it wouldn’t work. But in a new study, experts at Equinor and Applied Petroleum Technology (APT) suggest they are on the brink of using contaminated drill cuttings to predict critical reservoir fluid properties. The two Norwegian firms believe their project will soon make it possible to treat cuttings, collected from each well, as if they were pressure/volume/temperature (PVT) samples, which are collected from a significantly smaller share of wells due to the risks and high cost of doing so. Since its inception in 1972, Equinor has drilled over 5,000 offshore wells, from which just over 2,000 PVT samples have been gathered. Given that only two or three good samples are taken from most wells—almost always exploration wells—a significant data gap exists for the remainder, most of which are the subsequent development wells. And each year, Equinor drills around 20 to 25 new wells, mostly near existing platforms. If oil and gas firms could obtain PVT-like results from cuttings alone, they would presumably gain more precise guidance on where to place new wells along with insights on facility/topside design, reservoir management, and eventually on the plugging and abandonment phase. The big obstacle Equinor and APT are trying to surmount centers on how rock cuttings are soaked in oil-based muds (OBMs) as they circulate up the wellbore before being collected from a shale shaker. The oil ends up masking the reservoir’s hydrocarbons, rendering traditional geochemistry methods ineffective. What Equinor has shown in its study is that problem can be circumvented to obtain reliable estimates of API gravity and viscosity, two defining aspects of reservoir fluid quality and flow potential. Several more outputs are in the works. Tao Yang, an industry-recognized reservoir expert and chief professional at Equinor, said when he spoke with geochemists in the company about the project nearly 2 years ago, they were decidedly dubious. “They gave us a warning at the beginning and said that this was a very difficult area, that the cuttings were too dirty, and that we probably wouldn’t get anything out of them,” he said. But what few in the upstream industry knew at the time is that there existed a technology that can overcome the contamination issue—and it has been around for decades. Invented in the 1960s, gel permeation chromatography (GPC) coupled with ultraviolet (UV) absorbance detection, or simply GPC-UV, is used primarily by the chemicals sector for polymer analysis. The GPC aspect separates molecules based on their size as they flow through a gel-filled column. The smaller molecules take the longest to pass through while the larger molecules move more rapidly—the opposite of how most geochemistry assays work. As the molecules move down the column, they are exposed to UV light. Depending on their chemical structure, each molecule will absorb light at varying intensities which in turn can be measured and used to detect the presence of various compounds, including those indicative of oil.

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.

Thermal equation of state of Li-rich schorl up to 15.5 GPa and 673 K: Implications for lithium and boron transport in slab subduction

Abstract The thermal equation of state (EoS) of natural schorl has been established at high temperatures up to 673 K and high pressures up to 15.5 GPa using in-situ synchrotron X-ray diffraction combined with a diamond anvil cell. The pressure-volume (P-V) data were fitted to the third-order Birch-Murnaghan EoS with V0 = 1581.45 ± 0.25 Å3, K0 = 111.6 ± 0.9 GPa and K′0 = 4.4 ± 0.2; additionally, when K′0 was fixed at a value of 4, V0 = 1581.04 ± 0.20 Å3 and K0 = 113.6 ± 0.3 GPa. The V0 (1581.45 ± 0.25 Å3) obtained by the third-order Birch-Murnaghan EoS agreed well with the measured V0 (1581.45 ± 0.05 Å3) under ambient conditions; this result confirmed the high accuracy of the experimental data in this study. Furthermore, the axial compression data of the schorl at room temperature were also fitted to a “linearized” third-order Birch-Murnaghan EoS, and the obtained axial moduli for the a- and c-axes were Ka = 621 ± 9 GPa and Kc = 174 ± 2 GPa, respectively. Consequently, the axial compressibilities were βa = 1.61×10-3 GPa-1 and βc = 5.75×10-3 GPa-1 with an anisotropic ratio of βa:βc = 0.28:1.00, indicating axial compression anisotropy. In addition, the compositional effect on the axial compressibilities of tourmalines was also discussed. Fitting our pressure-volume-temperature (P-V-T) data to the high-temperature third-order Birch-Murnaghan EoS provided the following thermal EoS parameters: V0 = 1581.2 ± 0.2 Å3, K0 = 110.5 ± 0.6 GPa, K′0 = 4.6 ± 0.2, (∂KT/∂T)P = -0.012 ± 0.003 GPa K-1 and αV0 = (2.4 ± 0.2) × 10-5 K-1. The obtained thermal EoS parameters in this study were also compared with those of previous studies on other tourmalines. The potential factors influencing the thermal EoS parameters of tourmalines were further discussed.

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

Phase envelopes in reservoir fill analysis: Two contrasting scenarios

Phase envelopes are routinely employed by reservoir engineers for fluid characterisation. These envelopes are controlled by reservoir fluid composition, pressure and temperature. As a result of increasing source-rock maturation, fluids with decreasing molecular weights and densities and increasing gas-to-oil ratios (and hence different phase envelopes) are generated, which are thus linked to fluid history. In addition to their importance for exploration, charge models can play a key role in constraining reservoir models and optimising field development, particularly when pressure–volume-temperature (PVT) data are properly integrated with fluid geochemistry. Two contrasting scenarios of fluid phase evolution from two different fields are presented, and their relations to charge analysis and reservoir models are discussed. The first example discusses the identification, based on hydrocarbon geochemistry complemented by overlapping modeled phase envelopes, of compartmentalised filling cycles in what was initially considered a single oil-rimmed gas accumulation. The second example presents an opposite scenario where two wet gas accumulations 20-km apart laterally and 400-feet average depth difference appear to represent a single more-expansive accumulation spread over areas of variable PVT conditions and reservoir qualities. The wet gas across both accumulations is characterised by a continuous phase evolution pattern that shrinks systematically (cricondentherm shifts to lower temperature and cricondenbar to lower pressure), suggestive of phase fractionation of a charge of single maturity. The proposed gas distribution model represents a discovery of a hybrid conventional and unconventional (tight sand) system, with potential for basin-centered gas. These findings provided better understanding of observed and projected fluids, impacting the development and completion plans by locating new gas producers. A recent well drilled midway between the two accumulations indeed tested wet gas, confirming fluid connectivity. Future work will attempt to link the gas distribution model with seismic attributes.

Flowing Bottomhole Pressure during Gas Lift in Unconventional Oil Wells

Summary We present artificial neural network (ANN) models for predicting the flowing bottomhole pressure (FBHP) of unconventional oil wells under gas lift operations. Well parameters, fluid properties, production/injection data, and bottomhole gauge pressures from 16 shale oil wells in Permian Basin, Texas, USA, are analyzed to determine key parameters affecting FBHP during the gas lift operation. For the reservoir fluid properties, several pressure-volume-temperature (PVT) models, such as Benedict-Webb-Rubin (BWR); Lee, Gonzalez, and Eakin; and Standing, among others, are examined against experimentally tuned fluid properties (i.e., viscosity, formation volume factor, and solution gas-oil ratio) to identify representative fluid (PVT) models for oil and gas properties. Pipe flow models (i.e., Hagedorn and Brown; Gray, Begs and Brill; and Petalas and Aziz) are also examined by comparing calculated FBHP against the bottomhole gauge pressures to identify a representative pipe flow model. Training and test data sets are then generated using the representative PVT and pipe flow models to develop a physics-based ANN model. The physics-based ANN model inputs are hydrocarbon fluid properties, liquid flow rate (qL), gas-liquid ratio (GLR), water-oil ratio (WOR), well true vertical depth (TVD), wellhead pressure (Pwh), wellhead temperature (Twh), and temperature gradient (dT/dh). A data-based ANN model is also developed based on only TVD, Pwh, qL, GLR, and WOR. Both physics- and data-based ANN models are trained through hyperparameter optimization using genetic algorithm and K-fold validation and then tested against the gauge FBHP. The results reveal that both models perform well with the FBHP prediction from field data with a normalized mean absolute error (NMAE) of around 10%. However, a comparison between results from the physics- and data-based ANN models shows that the accuracy of the physics-based model is higher at the later phase of the gas lift operation when the steady-state pipe flow is well established. On the contrary, the data-based model performs better for the early phase of gas lift operation when transient flow behavior is dominant. Developed ANN models and workflows can be applied to optimize gas lift operations under different fluid and well conditions.

Phase Behaviors of Gas Condensate at Pore Scale: Direct Visualization via Microfluidics and In-Situ CT Scanning

Summary Gas condensate is stored in multiscale pores, fractures, and vugs within geological formations. Confinement within these structures significantly influences the phase behavior of gas condensate, rendering it challenging to characterize through conventional bulk pressure/volume/temperature (PVT) measurements. In this study, we used microfluidics and in-situ computed tomography (CT) scanning to directly measure the upper dewpoint of gas condensate and the gas/oil ratio in porous media during depressurization. We used two microfluidic chips with different pore sizes to investigate the confinement effects on gas condensate phase behavior at various scales, including pores as small as 50 nm. Our results revealed a significant increase in the upper dewpoint within the pores compared to bulk PVT measurements, with a more pronounced deviation at smaller pore sizes. Additionally, the proportion of condensate oil in porous media exceeded that observed in bulk PVT measurements at the same pressure. To validate our microfluidic findings, we conducted in-situ CT scanning experiments using a porous media model created by packing quartz particles. CT scans revealed pores ranging from a few micrometers to over 100 micrometers. Consistently, we observed an increase in the upper dewpoint and liquid ratio within these pores. Our study provides crucial experimental evidence indicating that the phase behavior of gas condensate in porous media deviates from bulk PVT measurements. The observed increase in the upper dewpoint, even within micrometer-sized pores, has important implications for phase equilibrium calculations.