Developing A Field with Asphaltenes Risks: From Diagnosis to Cure

Rondon, Marianna; Kane, Moussa; Quintero, Carlos; Passade-Boupat, Nicolas; Zhou, Honggang

doi:10.2118/171891-ms

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Figure 6 shows the classification graph using the risk line reported by Rondon-Gonzalez et al 19 (red dotted line, SPE-171891), indicating that the Colombian crude data considered in this work are not properly classified. However, it was identified that the data of the fluids that precipitate and those that do not precipitate are spatially separated in the graph.…”

Section: Classification Model To Predict the Asphaltene Damage Riskmentioning

confidence: 92%

“…Therefore, the first part of this study was focused on developing a suitable screening model. Rondon-Gonzalez et al 19 proposed an asphaltene precipitation risk classification model based on the density at reservoir conditions of a fluid and the results of the oil differential precipitation (ASCI) tests. The model consists of a 2D graph (in situ density vs ASCI) in which the risk of precipitation is determined, classifying the fluids according to their location on this graph.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Classification Model To Predict the Asphaltene Damage Riskmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Some examples of the usefulness, for asphaltene deposition prediction or emulsion stability studies, of this index has been given in Society of Petroleum Engineers (SPE) papers. − …”

Section: Introductionmentioning

confidence: 99%

Could the Asphaltene Solubility Class Index Be Used as the “Wax Appearance Temperature” of Asphaltenes? Illustration through the Study of the Polydispersity of PetroPhase 2017 Asphaltenes

et al. 2018

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Being able to assess the solubility properties of asphaltenes is essential for oilfield asphaltene risk assessment. The solubility distribution of PetroPhase 2017 asphaltenes was characterized using the asphaltene solubility class index (ASCI), which a simple methodology based on precipitation onset determination in different heptols. This ASCI method was also used to measure the polydispersity in terms of solubility for PetroPhase 2017 asphaltenes that were fractionated from the native crude oil with precipitants of different qualities. Similar to other crude oils, asphaltenes from PetroPhase 2017 have a high level of polydispersity, ranging from fractions “insoluble in toluene” to “soluble in heptane”, which may seem contradictory with the usual definition of asphaltenes. The modification of the precipitant/crude oil ratio, with heptane as the precipitant, also highlighted the presence of co-precipitated materials that did not affect the solubility class of asphaltenes. From our experience over the last 15 years, using ASCI or another equivalent tool, would greatly improve the comparison between different studies on asphaltene, similar to the well-known wax appearance temperature, which is measured and reported in the vast majority of studies on paraffins.

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

Tange,

Hiraiwa,

Khlaifi

et al. 2023

SPE Journal

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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.

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Developing A Field with Asphaltenes Risks: From Diagnosis to Cure

Cited by 4 publications

References 3 publications

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

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

Could the Asphaltene Solubility Class Index Be Used as the “Wax Appearance Temperature” of Asphaltenes? Illustration through the Study of the Polydispersity of PetroPhase 2017 Asphaltenes

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

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