Asphaltene Onset Pressure Research Articles

WITHDRAWN: Comparative Analysis of Asphaltene Deposition Methods : Stability indices, Stability plots, and Equation of state

Asphaltene deposition is one of the major problems that hinder hydrocarbon production. It can occur anywhere between the reservoir and the surface. Therefore, it is imperative that there be a procedure for minimizing the deposition of asphaltene in the production flow lines. There are several screening criteria that can be considered to determine the asphaltene deposition. This research will conduct a comprehensive study on different methodologies to determine the stability of asphaltene: the first category is the stability criteria using indices such as: the colloidal instability index (CII), the colloidal stability index (CSI), the stability index (SI), and the asphaltene stability predicting model (ANJIS). These four methods are used to predict the stability of asphaltene using SARA analysis. The fifth one is called modified CII which uses the compositions analysis of reservoir fluids, instead of SARA analysis, to determine the stability of asphaltene. The second method uses SARA Analysis to predict the stability via plots such as: Stankiewics plot (SP), De Boer plot, Sepulveda stability criterion (SCP). The third method is using compositional analysis of reservoir fluids and the equation of state model to predict the asphaltene onset pressure (AOP). In the last method Soave Redlich Kwong (SRK) equation of state model was used to predict asphaltene precipitations.In this work, a dataset of 166 was created which includes reservoir information, depth, compositional analysis, and SARA analysis. These data are used to study the potential of asphaltene deposition at the reservoir level. It was found that the stability criteria using indices and plots shows discrepancy in the results. The only two methods that show all the datasets are stable are the modified CII and the EOS model. As far as predicting asphaltene stability is concerned, the EOS method, based on compositional analysis and the thermodynamic model, appears to be the most reliable and trustworthy method. The other methods presented are observational rather than thermodynamic. Using these criteria is intended as a quick and fast estimate of the stability potential of asphaltene, not for planning or management of asphaltene deposition issues.

Comprehensive approach for assessing, managing, and Monitoring Asphaltene Deposition and inhibitor utilization in oil wells

This study addresses the challenges of managing asphaltene precipitation in oil well production, aiming to establish a rapid computational approach for production management based on individual well productivity. The methodology involves modeling the impact of inhibitors on tubing diameter and incorporating well and reservoir performance. Validation using real-world data reveals optimal production conditions related to Asphaltene Onset Pressure (AOP) and Bubble Point Pressure (Pb) in relation to inhibitor location. The study emphasizes the importance of positioning AOP above the perforation interval for operable conditions and observes the impact of asphaltene deposition on wellhead pressure. To address increased bottomhole drawdown pressure, a production rate regulatory program is developed. The research offers a novel computational approach for managing asphaltene precipitation, grounded in real-world data, and provides insights into optimizing production rates while ensuring flow assurance in oil well operations. Additionally, strategic recommendations for field development are proposed, including acid/hydraulic fracturing and deviated horizontal/bilateral wells drilling to mitigate bottomhole drawdown pressure.

Association parameters for Cubic-Plus-Association equation of state determined by molecular dynamics and Petroleomics: Prediction of asphaltene precipitation

In this study, petroleomics characterization, molecular dynamics (MD) simulations, and a cubic-plus-association (CPA) equation of state (EoS) were conducted to understand how the structural characteristics affect the phase behavior of a fluid. To do this, the asphaltene onset pressure (AOP) was estimated by CPA EoS using association parameters determined from MD simulations. Petroleomics characterization and gas chromatography were conducted to identify appropriate representative molecular structures for describing a live crude oil. The American Petroleum Institute (API) gravity, dead and live crude oil densities, and viscosities at reservoir conditions were calculated from the MD simulation results and were found to differ from the experimental measurements by less than 5%, indicating accurate characterizations for both real dead and live crude oil. A formation damage diagnosis from a compositional perspective was conducted because oxygenated aromatic compounds, which are highly active in aggregation terms, were identified in the aromatic fraction by Fourier-transform ion cyclotron resonance mass spectrometry (FT–ICR MS). The association parameters for the self-associating organics (So: asphaltenes and oxygenated aromatics) and the cross-interaction between these Sos and the maltenes were calculated from the MD simulation results. To the best of our knowledge, this is the first time that association parameters have been obtained from an MD model and successfully implemented in a CPA EoS, allowing the prediction of AOP with an accuracy close to 1% compared to the experimentally determined value for live crude oil. This study provides a promising framework to develop an EoS for crude oils based on their molecular characteristics, reducing the need to perform parametric tuning with experimental data. In addition, this structural characterization proposed could be important to other key topics such as enhanced oil recovery (EOR) and carbon capture use and storage (CCUS), relating the structural composition to phase behavior.

Asphaltene Flow Assurance Risk Evaluation Case Study to Establish Guidelines for CCUS in Arabian Gulf Carbonate Oil Field

Summary In this paper, we present a case study for a comprehensive series of asphaltene flow assurance risk evaluations in the Arabian Gulf Carbonate Oil Field, where CO2 enhanced oil recovery (EOR) is recognized as one of the highest potential technologies for full-field implementation. At first, the sampling location was carefully selected considering the target reservoir’s features because the reliability of asphaltene studies highly depends on sample representativeness. After the quality assurance and quality control of collected samples, asphaltene onset pressures (AOPs) were measured at multiple temperatures under CO2 mixing conditions. The experimental design was optimized in terms of not only improving evaluation accuracy but also minimizing the experimental/sampling cost. The AOP measurements showed clear potential risks associated with CO2 injection. Subsequently, the numerical model analysis was conducted with the cubic-plus-association equation of state model to identify the risk area during CO2 injection. The analysis suggested that risk area is applicable to not only near-wellbore region at the sampling location but also tubing section/surface facility. Furthermore, risk level can be higher at the deeper location of the target reservoir. Finally, CO2-induced asphaltene formation damage risk was investigated from the viewpoints of precipitated asphaltene particle size and pore throat size in the porous media. As a result, the clogging risks from CO2-induced asphaltene were estimated to be high in the target reservoir. By virtue of the comprehensive risk evaluation, the asphaltene flow assurance risk associated with CO2 injection was identified field-wide. The findings from the evaluation encouraged us to move on to future actions, such as more detailed formation damage risk evaluation and mitigation plan development. The extended asphaltene risk evaluation covering the precipitation to deposition phenomena and the reverse engineering for sampling operations based on the experimental design made a worthy demonstration to reduce unnecessary cost and time while obtaining the key information to drive the project. The procedure in this work can contribute to establishing a subsurface part of a guideline for carbon capture, utilization, and storage (CCUS) from the viewpoint of asphaltene flow assurance risk evaluation.

Asphaltene precipitation described with a Yukawa SAFT-VR/MSA equation of state

We combine the Statistical Associating Fluid Theory for Potentials of Variable Range (SAFT-VR) and the Mean Spherical Approximation (MSA) to develop an equation of state to model asphaltene precipitation in petroleum fluids including the measured asphaltene onset pressures (AOP’s) of a reservoir live oil. In this model, dispersive solvent–solvent and asphaltene–solvent interactions in reservoir fluid mixtures are represented by Yukawa potentials. The approach is based in the modeling of polar and ionic interactions via Yukawa potentials, as originally proposed by the Generalized Mean Spherical Approximation Theory, and also in recent studies in molecular simulation of charged fluids using non-Ewald effective pair interactions, as in the Wolf method.

Screening and Investigation on Inhibition of Sediment Formation in a Kuwait Light Crude Oil by Commercial Additives with Some Guidelines for Field Applications

The precipitation of asphaltene and waxes occurs when crude oil characteristics change as a consequence of pressure, temperature variations, and/or chemical modifications, etc. The costs associated with the cleaning of deposition on the production equipment and the loss of profit opportunities can go beyond hundreds of millions of USD. Thus, there is a strong incentive to search for ways to mitigate deposit formation during the crude production process. A light crude bottom hole fluid sample from a deep well with an asphaltene deposition problem was analyzed in the laboratory. Basic data on density, viscosity, bubble point, GOR, and asphaltene onset pressure were measured at a PVT laboratory. Asphaltene characterization, as a prescreening for appropriate inhibitors, has been conducted using asphaltene phase diagrams (APD). The APD generated from two developed software programs in both Matlab and Excel codes were favorably compared with the phase behavior of other oil samples available in the literature and has shown to be an excellent match. Various test methods were used to demonstrate the asphaltene instability of the oil samples. Eleven chemical inhibitors from five global companies were screened for testing to inhibit the precipitation. The optimum concentration and the amount of reduction in precipitation were determined for all of these chemicals to identify the most suitable chemicals. Finally, some recommendations are given for the field application of chemicals.

Phase Behavior and Reversibility Mechanisms of Asphaltene Precipitation for High-Pressure High-Temperature CO2–Oil Systems

Net-zero carbon emission pledges by 2050 are prompting researchers to investigate alternatives to store CO2 safely. Subsurface CO2 storage faces many challenges, especially when the geological space is a depleted oil reservoir. A crucial problem is the newly formed asphaltenes during CO2 injection that could limit or even block the rock-effective zone for gas storage. In this work, a novel pressure–volume–temperature cell and a solid detection system are used to measure the phase behavior, asphaltene onset pressure, and asphaltene precipitation. Results show four different equilibrium phases during the isothermal depressurization processes. Likewise, asphaltene precipitation increases with CO2 fractions and decreases with temperature, reaching its maximum at the bubble point. More importantly, the redissolution of asphaltene particles back into the solution is observed, revealing the formation process reversibility at certain conditions (25 mol % CO2 at 60, 90, and 120 °C; 35 mol % CO2 at 90 and 120 °C). The Peng–Robinson equation of state model is employed to model the fluid phase, while the solid phase equilibrium is modeled with a solid model. A new correlation for the asphaltene molar volume is proposed, which predicts the experimental data with an AARD of 0.7%.

Integrated Machine Learning Model for Predicting Asphaltene Damage Risk and the Asphaltene Onset Pressure

Asphaltene precipitation can promote a drastic reduction in oil production because of asphaltene precipitation and deposition damage. Therefore, screening models to predict the risk of asphaltene damage and equations of state (EoS) to predict the asphaltene onset pressure (AOP) are useful to prevent production drops and optimize the management of oil resources. Most asphaltene screening models have been focused on the oil compositions (SARA analysis); however, these screening models do not consider key variables for asphaltene stability such as the temperature, pressure, well depth, gas–oil ratio, and so on. As the EoS are typically based on experimental data, to fit parameters needed to reproduce the experimental AOP, expensive laboratory analyses are required for this objective. In this study, a classification machine learning (CML) model based on support vector machines was proposed to predict the asphaltene damage risk from the asphaltene stability class index data and the in situ live crude oil densities. In addition, a model based on linear regression (MLR) to predict AOP from the reservoir pressure, saturation pressure, temperature, and some in situ live crude oil compositions was proposed. In total, 24 crude oils were evaluated experimentally to propose the classification model, and a perfect classification accuracy (100%) was obtained in both cases. The CML results were compared to compositional screenings, where classification accuracy was between 29 and 88%, the best accuracy being obtained from the well-known de Boer plot. The MLR model was obtained from data from 53 live crude oils, using hypothesis tests to select the statistically representative characteristics regarding AOP, and a determination coefficient of 0.77 was obtained. The proposed integrated ensemble model contributes to predicting the potential risk of damage due to asphaltene precipitation and estimating a pressure range where these asphaltenes precipitate, allowing the necessary preventive measures to be taken to avoid an oil production decline.

The hard-core effect on PC-SAFT to model asphaltene precipitation in petroleum reservoir fluids

In this work, a hard-core version of the Perturbed Chain-Association Fluid Theory Equation of State (PC-SAFT EoS) is proposed and used to predict upper asphaltene onset pressures (AOPs), bubble points (BPs), and precipitated asphaltene amounts for eighteen real petroleum reservoir fluids around the world. As a result, for the same assumed asphaltene molecular weight (MW) value, this hard-core PC-SAFT model predicts lower AOPs than the other two PC-SAFT versions here tested, including the original version of PC-SAFT. The AOPs and precipitated asphaltene amounts are found to be directly related to the asphaltene MW values used for each PC-SAFT model. The hard-core PC-SAFT model here presented is the PC-SAFT version that requires the lowest asphaltene MWs and predicts the lowest precipitated asphaltene amounts for the eighteen fluids modeled.

A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection.

Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas–oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (R2: 0.793, RMSE: 7.5) and the SVM models (R2: 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements.

Experimental investigation and thermodynamic modeling of asphaltene precipitation during pressure depletion and gas injection at HPHT conditions in live oil using PC-SAFT EoS

Due to the high costs associated with asphaltene precipitation and deposition, the study of this flow assurance problem has always been a major issue in the petroleum industry. We studied the asphaltene phase behavior in four Iranian live oil samples. First, asphaltene onset pressure and the amounts of the precipitated asphaltene during pressure depletion and gas injection at high-pressure high-temperature (HPHT) conditions were measured, experimentally. Then, thermodynamic modeling of asphaltene precipitation was performed using the perturbed chain form of the statistical associating fluid theory (PC-SAFT). In previous researches, PC-SAFT equation of state (EoS) along with the standard method of characterization was applied to light crude oil having low asphaltene content only, while in this work, three of the samples were heavy oil with high asphaltene content. To perform the thermodynamic modeling, the oil samples were characterized by two different methods. In the first method, the adjustable parameters of the model, which are the aromaticity of aromatics + resins and the aromaticity of asphaltene, were adjusted separately. In the second method, both of the parameters of the model were tuned simultaneously, as one of the novelties of this research. Then the amounts of precipitated asphaltene were modeled using the aforementioned characterization methods, and the results were compared to the experimental data. According to the results, simultaneous adjustment of the model parameters in the heavy oil samples could improve the average absolute deviation (AAD) by 5% - 12%. However, no remarkable improvement was observed for the light oil sample. For two of the oil samples, the proposed model showed a good agreement with the experimental data as the calculated AAD were 14% and 26%. For the other oil samples, the trend of the experimental data was predicted by the model, but the values of the experimental data could not be matched. We were able to solve this problem by introducing a confidence factor for experimentally measured data. By considering this weight factor, in one of the samples, the AAD was reduced by about 70% and reached 24%, and in other oil sample, the error decreased by 90% and reached 31%.

Development of a Downhole Measurement System for Phase Behavior of Reservoir Crude Oils and Retrograde Condensates

This article reviews the scientific findings that were integrated into a downhole prototype to measure hydrocarbon phase behavior. While laboratory measurements of bubble point pressure (Pb), asphaltene onset pressure (AOP), and dewpoint pressure (Pd) are well established and have been extensively studied, such measurements of reservoir crude oils are novel downhole due to their operational, hydraulic, and interpretational complexities. Furthermore, such laboratory measurements typically take several hours to complete and can only be carried out by skilled personnel. For the downhole measurement, novel science and understanding were developed to reduce the measurement time to minutes and to automate the workflow. This article reviews the science and technology that enabled these downhole measurements to be made rapidly and provides field examples of how sampling is optimized and quality is validated in real time.

Performance evaluation and improvement of PC-SAFT equation of state for the asphaltene precipitation modeling during mixing with various fluid types

One of the significant problems of the upstream flow assurance community is precipitation or deposition of asphaltene that leads to affect the economics of oil production seriously. Knowledge of asphaltene onset pressure (AOP) and amount of asphaltene precipitation in the change of temperature, pressure, and composition is the essential information for preventing this problem. Recent studies have shown that thermodynamic models based on the solubility nature of asphaltenes using an equation of state (EOS) are the most effective approach to asphaltene behavior modeling. In this research, the common version of the SAFT equation, known as the Perturbed Chain form of Statistical Association Fluid Theory (PC-SAFT) EOS without using association terms, has been employed and evaluated during the pure gas and other fluid types injection or mixing. In the case of pure gas injection (single components, for instance, N2 or CO2 injection), the model results have shown that using the reference values of the PC-SAFT parameters (m, σ, ε/k), those are reported in the literature, may not predict the weight percentage of the asphaltene precipitation accurately. It will be shown that considering these parameters as the adjustable variables can improve the model prediction for the asphaltene precipitation behavior. Furthermore, a new methodology has been suggested to characterize the injection fluid while its SARA (Saturates-Aromatics-Resins-Asphaltenes) analysis is not reported. This methodology is established in accordant with the material balance concept; in fact, the distribution of SARA fractions for the injection fluid will be estimated from the characterized crude oil. Six systems of crude oil and injection fluid, including pure N2, pure methane, pure CO2, wet gas, and liquid condensate, have been taken under study to evaluate the accuracy and flexibility of the proposed methodology by comparing to the available asphaltene experimental data.

Higher-order compositional simulation of asphaltene damage and removal in the wellbore by the CPA-EOS

Simulation of asphaltene damage around the wellbore is important in relation to well productivity. Existing numerical approaches rely on the surface adsorption of asphaltene on the rock using a Langmuir isotherm. We demonstrate that asphaltene accumulation around the wellbore can be modeled as an asphaltene-rich liquid phase without the need to include surface adsorption explicitly. The relative permeability concept accounts for surface adsorption as well as pore-plugging. Our approach is fully verified with recent experimental data. This work is based on the fact that below the asphaltene onset pressure, an asphaltene-rich phase is formed with much higher viscosity than the oil phase from which it evolves. We perform phase behavior calculations using the cubic-plus association equation of sate (CPA-EOS). Flow and species transfer between the phases are simulated based on the higher-order method with high accuracy and low numerical dispersion. The algorithm includes full 3D unstructured gridding to model well damage and frequency and extent of asphaltenes removal in field scale applications. An example with a fracture crossing the producing well is included to examine deposition in the fracture and the matrix.

Simultaneous Control of Formation and Growth of Asphaltene Solids and Wax Crystals Using Single-Walled Carbon Nanotubes: an Experimental Study under Real Oilfield Conditions

Asphaltene and wax depositions in oil production systems have been recognized as the most serious flow assurance concern of petroleum engineering. Despite the several chemicals offered for treatment of issues induced by these organic deposits, proposing a single efficient chemical to control simultaneously both asphaltene and wax precipitation has not been attended yet. Here, single-walled carbon nanotubes (SWCNTs) were synthesized to assess their effects on controlling the formation and size growth of asphaltene particles and wax crystals in live oil under realistic production conditions. SDS experiments showed that during depressurization at 125 °C, the asphaltene onset pressure decreased from 5946 psi in untreated live oil to 4375 in treated live oil with 100 ppm SWCNTs. High-pressure microscopy revealed that SWCNTs efficiently controlled the growth of asphaltene aggregates and reduced the size of asphaltenes in live oil during depressurization at 125 °C. This is ascribed to the strong π–π interactions between the aromatic rings of SWCNTs and electron clouds of asphaltenes. Results of cross-polarized microscopy showed that SWCNTs at an optimum concentration of 75 ppm postponed the wax formation temperature and gelation point from 32.6 and 21.5 °C to 23.2 and 14.7 °C, respectively. In addition, the morphology of the wax crystals with an average size of 756 nm in untreated oil changed from dense spherical-like into thin needle-like with an average size of 86 nm in treated oil with SWCNTs at 75 ppm. The changes in the wax crystal size and structure via X-ray powder diffraction analysis after crude oil treatment with SWCNTs confirm interactions between the carbon nanotubes and active sites of the wax crystals. The findings of this research prove that synthesized carbon nanotubes can be used as efficient inhibitor and dispersant agents for stabilizing the waxes and asphaltenes in live oil with controlling the rate of aggregation under high-pressure–high-temperature conditions.

Fiberglass-Lined Tubing Helps Prevent Asphaltene Deposition in Oil Wells

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 21441, “First-Time Worldwide Application of Glass-Reinforced Epoxy-Lined Tubing for Prevention of Asphaltene Deposition in Tubing in Oil Wells: A Case Study From Kuwait,” by Reji E. Chinnappan, SPE, Milan Telang, SPE, and Riyad Quttainah, SPE, Kuwait Oil Company, et al., prepared for presentation at the virtual 2021 International Petroleum Technology Conference, 23 March–1 April. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. Asphaltene deposition in production tubing represents a major flow-assurance challenge. Common strategies to mitigate asphaltene deposition downhole include mechanical or solvent cleanouts and chemical inhibition. These are associated with production deferment, high job costs, safety and environmental risks, and operational issues. An operator has addressed this challenge using production tubing lined with glass-fiber-reinforced epoxy (GRE). This technology was implemented in two trial wells. The paper describes the different mitigation strategies employed by the operator and presents the findings of successful trials. Background Jurassic wells of a Kuwait Oil Company asset are producing light crude from a tight matrix-type reservoir located at a depth of 13,000–15,000 ft. Reservoir pressure has depleted from approximately 9,500–10,000 psi to approximately 6,000 psi because of sustained production in the absence of any significant pressure support. Oil production rates per well have diminished to the 500- to 1,000-BOPD range. The oil features high asphaltene onset pressures (4,000–5,000 psi). When considering time-lapse plots of caliper logs from a well where asphaltene deposition used to occur, the plot indicates that significant asphaltene deposition in the well took place below 4,500 ft and progressively increased over time. In approximately 5 months, the average internal diameter of the tubing reduced from 2.75 in. to less than 2 in., thereby constricting the flow significantly and requiring cleaning of the tubing. In extreme cases, the tubing string could be fully plugged. Many field trials with different tools and chemicals using batch and continuous treatment have been conducted in past years to solve this problem but without satisfactory results. Application of GRE-Lined Tubing for Asphaltene Control The operator decided to apply a novel strategy of using tubing internally lined with GRE based on its established ability to retard, and even eliminate, scale nucleation and deposition. The technology uses a thin-walled, solid-filament-wound GRE/fiberglass tube run inside carbon steel production tubing. Cement is pumped into the annulus between the steel tubing and the GRE liner. The ability to prevent asphaltene from sticking to the inner wall of the tubing is attributed to the smoother internal surface. It is also corroborated by a higher Hazen Williams coefficient value of 150 for GRE as compared with 110 for carbon steel pipe, which provides for lesser frictional pressure loss during flow. The GRE liners used by the operator have a surface roughness of 0.00011 in., which, unlike bare steel, is retained over the life of the GRE. The GRE-lined tubing proved to withstand temperatures of up to 280°F and hydrogen sulfide concentrations of up to 50%. This is comfortably more than the process conditions for the trial wells in consideration.

Laboratory Investigation of Permeability Impairment Caused by Asphaltene Precipitation during Screening for Gas-Injection Enhanced Oil Recovery and Pressure Change in a Major Gulf of Mexico Field

Summary As part of studying miscible gas injection (GI) in a major field within the Green Canyon protraction area in the Gulf of Mexico (GOM), asphaltene-formation risk was identified as a key factor affecting a potential GI project. The industry has not conducted many experiments to quantify the effect of asphaltenes on reservoir and well performance under GI conditions. In this paper we discuss a novel laboratory test for evaluating the asphaltene effect on permeability. The goals of the study were to define the asphaltene-precipitation envelope using blends of reservoir fluid and injection gas, and measure permeability reduction caused by asphaltene precipitation in a core under GI. To properly analyze the effect of GI, a suite of fluid-characterization studies was conducted, including restored-oil samples, compositional analysis, constant composition expansion (CCE), and differential vaporization. Miscibility conditions were defined through slimtube-displacement tests. Gas solubility was determined through swelling tests complemented by asphaltene-onset-pressure (AOP) testing. The unique procedure was developed to estimate the effect of asphaltene deposition on core permeability. The 1-ft-long core was saturated with the live-oil and GI mixture at a pressure greater than the AOP, and then pressure was depleted to a pressure slightly greater than the bubblepoint. Several cycles of charging and depletion were conducted to mimic continuous flow of oil along the path of injected gas and thereby to observe the accumulation of asphaltene on the rock surface. The test results indicated that during this cyclic asphaltene-deposition process, the core permeability to the live mixture decreased in the first few cycles but appeared to stabilize after Cycle 5. The deposited asphaltenes were analyzed further through environmental scanning electron microscopy (ESEM), and their deposition was confirmed by mass balance before and after the tests. Finally, a relationship was established between permeability reduction and asphaltene precipitation. The results from the asphaltene-deposition experiment show that for the sample, fluids, and conditions used, permeability is impaired as asphaltene flocculates and begins to coat the grain surfaces. This impairment reaches a plateau at approximately 40% of the initial permeability. Distribution of asphaltene along the core was measured at the end by segmenting the core and conducting solvent extraction on each segment. Our recommendation is numerical modeling of these test results and using this model to forecast the magnitude of the permeability impairment in a reservoir setting during miscible GI.

An effective asphaltene precipitation modeling approach using PC-SAFT with detailed fluid descriptions for gas injection conditions

Fluid characterization plays a prominent role in identifying the properties of the crude oil, particularly the equilibrium conditions. It gains more importance, especially in problematic crudes that, for instance, contain precipitating constituents, like Asphaltene. Aside from the pressure depletion, enhanced Asphaltene precipitation during the miscible gas injection causes many problems to the production system. Therefore, the employed characterization procedure must be able to predict the Asphaltene instability conditions effectively. Recently a class of solution techniques using the perturbed chain statistical associating fluid theory (PC-SAFT) and dealing with Asphaltene phase as a liquid-like phase have gained attractions in literature. However, such techniques could not consider the effect of the injected gas composition and structure, because they lumped all the heavy gas components in a single pseudo-component. Hence, in this study, a detailed characterization method is proposed that can account for the heavier gas components. The modeling approach in this work, assumes the liquid-like Asphaltene framework and PC-SAFT for phase behavior calculations. In this work, the three PC-SAFT parameters for Asphaltene and Aromatic + Resin are simultaneously regressed with gas injection, to match the saturation and Asphaltene onset pressures with experimental data. The outcomes of this study are compared with the experimental data and previous characterization models. The results prove the ability of the detailed characterization approach for predicting the bubble point and Asphaltene onset pressures. It not only leads to lower estimation errors in most cases but also can capture the actual trend of bubble and Asphaltene onset curves, which makes it even more reliable for the prediction of instability conditions.

A Wavelet-Based Model for Determining Asphaltene Onset Pressure

Asphaltene onset pressure (AOP) is a significant parameter for determining the flow assurance of live oils. The solid detection system (SDS) is one of the prevalent techniques used by service laboratories to evaluate the stability of asphaltenes under reservoir conditions. The determination of AOP based on this technique entails the interpretation of recorded data, making the accuracy of the result prone to error. Accordingly, this research aimed to provide a robust computational method for determining AOP by wavelet analysis of SDS data. Changes in the curvature of transmitted light (CTL) were considered a diagnostic criterion to detect AOP. To substantiate this hypothesis, CTL was first calculated at each pressure. The discrete wavelet transform was then applied to decompose the CTL curve and compute the CTL entropy $$\left( {E_{\text{CTL}} } \right)$$ based on the decomposition results. Finally, a relation was established between AOP and the entropy variations of CTL $$\left( {\Delta E_{\text{CTL}} } \right)$$ , leading to the AOP determination model. This model indicated that the maximum value of $$\Delta E_{\text{CTL}}$$ is at AOP. Put differently, the onset of asphaltene precipitation pressure corresponds to the highest variation in the CTL entropy. The results obtained from the AOP determination model in various reservoirs are consistent with the experimental findings.

An experimental and theoretical investigation of asphaltene precipitation in a crude oil from the Brazilian pre-salt layer under CO2 injection

The asphaltene onset pressure (AOP) is a key parameter to determine the flow assurance of oils. In this work we performed an experimental study of the PVT (pressure–volume-temperature) properties of a crude oil from the Brazilian pre-salt layer. First, dead-oil was recombined with natural gas at the reservoir gas-oil ratio (GOR). Then, different techniques (volumetric, visual, light-scattering and filtration) were used to measure the AOP and bubble pressure (BP) at temperatures in the range of (293.15 to 348.15) K. The effect of CO2 injection on both properties was also evaluated at reservoir temperature. Additional data of mass density was measured at reservoir pressure and temperature. The experimental data were used to fit the adjustable parameters of the Peng–Robinson equation of state (PR-EoS) and the calculated results are compatible with the experimental data. The PR-EoS calculates a decrease in the AOP and an increase in the BP as temperature rises. PR-EoS correctly predicts the qualitative effect of gas injection on the studied properties: all evaluated properties increase as CO2 is injected in the recombined oil.