scholarly journals Multiphase flow modeling of asphaltene precipitation and deposition

2018 ◽  
Vol 73 ◽  
pp. 51 ◽  
Author(s):  
Amir Tabzar ◽  
Mohammad Fathinasab ◽  
Afshin Salehi ◽  
Babak Bahrami ◽  
Amir H. Mohammadi
Keyword(s):  
Multiphase Flow ◽  
Crude Oil ◽  
Flow Rate ◽  
Asphaltene Precipitation ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Reservoir Fluid ◽  
Point Pressure ◽  
Component C ◽  
Onset Pressure

Asphaltene precipitation in reservoirs during production and Enhanced Oil Recovery (EOR) can cause serious problems that lead to reduction of reservoir fluid production. In order to study asphaltene tendency to precipitate and change in flow rate as a function of distance from wellbore, an equation of state (Peng-Robinson) based model namely Nghiem et al.’s model has been employed in this study. The heaviest components of crude oil are separated into two parts: The first portion is considered as non-precipitating component (C31A+) and the second one is considered as precipitating component (C31B+) and the precipitated asphaltene is considered as pure solid. For determination of the acentric factor and critical properties, Lee-Kesler and Twu correlations are employed, respectively. In this study, a multiphase flow (oil, gas and asphaltene) model for an asphaltenic crude oil for which asphaltene is considered as solid particles (precipitated, flocculated and deposited particles), has been developed. Furthermore, effect of asphaltene precipitation on porosity and permeability reduction has been studied. Results of this study indicate that asphaltene tendency to precipitate increases and permeability of porous medium decreases by increasing oil flow rate in under-saturated oil reservoirs and dropping reservoir pressure under bubble point pressure. On the other hand, asphaltene tendency to precipitate decreases with pressure reduction to a level lower than bubble point pressure where asphaltene starts to dissolve back into oil phase. Moreover, it is observed that precipitation zone around the wellbore develops with time as pressure declines to bubble point pressure (production rate increases up). Also, there is an equilibrium area near wellbore region at which reservoir fluid properties such as UAOP (Upper Asphaltene Onset Pressure) and LAOP (Lower Asphaltene Onset Pressure) are constant and independent of the distance from wellbore.

Determination of the Asphaltene Precipitation Envelope and Bubble Point Pressure for a Mexican Crude Oil by Scanning Transitiometry

2013 ◽  
Vol 27 (3) ◽  
pp. 1212-1222 ◽  
Author(s):  
Marco A. Aquino-Olivos ◽  
Jean-Pierre E. Grolier ◽  
Stanislaw L. Randzio ◽  
Adriana J. Aguirre-Gutiérrez ◽  
Fernando García-Sánchez
Keyword(s):  
Crude Oil ◽  
Asphaltene Precipitation ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure ◽  
Scanning Transitiometry

Determination and Prediction of Bubble Point Pressure for CO2-Oil System

2010 ◽  
Vol 121-122 ◽  
pp. 1002-1005
Author(s):  
Yong Mao Hao
Keyword(s):  
Calculation Result ◽  
Model Simulation ◽  
Co2 Concentration ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Reservoir Fluid ◽  
Single Curve ◽  
Point Pressure ◽  
The One ◽  
Comprehensive Study

Through experiments and model simulation, this paper had a comprehensive study on the determination and prediction of the bubble point pressure(BPP) for the CO2-oil system. The result indicates that The reservoir fluid and CO2 can normally reach the one-contact miscible state above the CO2 concentration of 60 mol%. For different oil samples of a certain reservoir, the curves of the relative BPP can be regressed to get a single curve. These curves can be used to estimate the BPP of any unknown oil-CO2 mixture in the reservoir. The calculation result of the advanced PREOS is accord with the experiment result, which is better model simulation.

Overcoming Downhole Fluid Sampling Challenge Using Dual-Port Straddle Packer in Shallow Viscous Reservoir

2021 ◽  
Author(s):  
Jose Gregorio Garcia ◽  
Ramil Mirhasanov ◽  
Shahad Waleed AlKandari ◽  
Abdullah Al-Rabah ◽  
Ahmad Al-Naqi ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Flow Rate ◽  
Flow Area ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Rock Stability ◽  
Viscous Oil ◽  
Point Pressure ◽  
Large Flow ◽  
Fluid Sampling

Abstract Objectives/Scope: Downhole fluid sampling of high quality, low contaminated oil samples with a pumpout wireline formation tester (PWFT) in a shallow unconsolidated reservoir with high H2S, high water salinity and filled with viscous oil is a quite challenging operation. Key properties, related to fluid flow in low pressure reservoirs: formation mechanical weakness, drilling invasion and the high contrast on fluid mobility, have resulted in the failure or impracticality of conventional methods for efficient sampling, resulting in a long sampling time causing high rig cost overhead and often highly contaminated oil samples. Most common problems faced during sampling are: Sand production- causing caving and lost seals and no pressure or samples. Sand plugging of the tool flowline. Operation limitation of pressure drawdown- dictated by extremely low formation pressure and mainly due to having saturated pressure around 20 to 30 psia below formation initial pressure (based on 118 bubble point samples measured in the laboratory). To maintain rock stability and low pressure draw down, fluids were pumped at a low rate, resulting in a long operation time, where a single sample take up to 15 – 20 hours of a pump out. Even with the long pumpout time the collected sample is often highly contaminated based on laboratory PVT analysis report. Methods, Procedures, Process Understanding of the formation properties and its rock mechanics helps to design proper operating techniques to overcome the challenge of viscous oil sampling in unconsolidated sand reservoir. A pre-job geomeechanical study of unconfined sand with very low compressive strength, restricted the flow rate to a maximum drawdown per square inch to maintain rock stability while pumping out. Dual-Port Straddle Packer (figure 1) sampling was introduced to overcome the mentioned challenges. Its large flow area (>1000 in² in 8 ½″ OH section) allowed a high total pumping rate while maintaining very low flow rate per square inch at the sand face, which resulted in an ultra-low draw-down flowing pressure to prevent sand collapse and producing below bubble point pressure that could invalidate further PVT studies. Packer inflation pressure has also been limited to a maximum of 150 to 200 psia above hydrostatic pressure to achieve isolation without overcoming the sand weak compressive strength. During the clean-out operation crude oil tend to separate from water based mud (WBM) filtrate in the packed-off interval due to fluid density difference and immiscibility of the two liquids due to the lower shear rate applied (among others). So a water/oil interface forms within the packed-off interval. As pumping continues, this oil/water fluid contact moves toward the bottom inlet port allowing more clean oil to accumulate at the top. Results, Observations, Conclusions: With the advantage of the dual inlet port straddle packer and the independent opening/closing operating design of each port, a clean segregated oil sample was collected from the top port at an early stage of job operation, saving rig time and cost without compromising collected fluids quality that is valid for PVT studies. Novel/Additive Information: Dual-port Straddle Packer with large flow area (plus filters) with ultra-low drawdown pressure to stay above bubble point pressure in shallow heavy oil reservoirs resulted to be another provided a cost effective technology that can be utilized for collecting downhole samples (DHS) that will undergo PVT studies.

Committee Machine-Ensemble as a General Paradigm for Accurate Prediction of Bubble Point Pressure of Crude Oil

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Sina Rashidi ◽  
Mohammad Khajehesfandeari
Keyword(s):  
Crude Oil ◽  
Phase Region ◽  
Accurate Prediction ◽  
Oil Reservoirs ◽  
Accurate Estimation ◽  
Two Phase ◽  
Committee Machine ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Point Pressure

Abstract Bubble point pressure (BPP) not only is a basic pressure–volume–temperature (PVT) parameter for calculation nearly all of the crude oil characteristics, but also determines phase-type of oil reservoirs, gas-to-oil ratio, oil formation volume factor, inflow performance relationship, and so on. Since the measurement of BPP of crude oil is an expensive and time-consuming experiment, this study develops a committee machine-ensemble (CME) paradigm for accurate estimation of this parameter from solution gas-oil ratio, reservoir temperature, gas specific gravity, and stock-tank oil gravity. Our CME approach is designed using a linear combination of predictions of four different expert systems. Unknown coefficients of this combination are adjusted through minimizing deviation between actual BPPs and their associated predictions using differential evolution and genetic algorithm. Our proposed CME paradigm is developed using 380 PVT datasets for crude oils from different geological regions. This novel intelligent paradigm estimates available experimental databank with excellent accuracy i.e., absolute average relative deviation (AARD) of 6.06% and regression coefficient (R2) of 0.98777. Accurate prediction of BPP using our CME paradigm decreases the risk of producing from a two-phase region of oil reservoirs.

scholarly journals NEW DERIVED CORRELATIONS FOR LIBYAN CRUDE OIL TO ESTIMATE BUBBLE-POINT PRESSURE

2019 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
Mustafa Sharrad ◽  
Hamid Hakim Abd-Alrahman
Keyword(s):  
Crude Oil ◽  
Oil Recovery ◽  
Production Performance ◽  
Petroleum Engineering ◽  
Empirical Correlations ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
New Correlation ◽  
Future Production ◽  
Point Pressure

The key factor of all petroleum engineering calculation is the knowledge of the PVT (Pressure, Volume, Temperature) parameters, such as determination of oil and gas flowing properties, predicting production performance in the future, production facilities designing and enhanced oil recovery planning methods. Those PVT properties are ideally determined experimentally in the laboratory. However, some of these experimental data is not always available; consequently, empirical correlations are used to estimate them. Many researchers have been focusing on models for predicting reservoir fluid properties from the available experimental PVT data, such as reservoir pressure, temperature, crude oil API gravity, gas oil ratio, formation volume factor, and gas gravity. The present study compares between some of the available empirical PVT correlations for estimating the bubble point pressure of some Libyan crude oils based on 35 data point samples from different Libyan oil fields. In the second part of this study, a new correlation has been derived to predict the bubble point pressure using Eviews software and compares the output results of this new correlation with some derived correlations found in the literature using statistical analysis such as the Average Absolute Error (AARE). The results showed an AARE as low as 8.7%, for bubble point pressure estimated by this new derived correlation. These results are valid to compare to other driven empirical correlations that have been evaluated. 

Developing a Model to Predict Oil Viscosity Using Specific Gravity and Formation Volume Factor as Correlating Parameters

2021 ◽  
Author(s):  
Henry Ijomanta ◽  
Olorunfemi Kawonise
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Fluid Composition ◽  
Oil Viscosity ◽  
Volume Factor ◽  
Bubble Point ◽  
Bubble Point Pressure ◽  
Reservoir Fluid ◽  
Point Pressure ◽  
Oil Density

Abstract This paper presents the research work on using a machine learning algorithm to predict the viscosity of Niger Delta oil reservoirs using formation volume factor and fluid density at bubble point pressure as correlating parameters. Oil Viscosity stands out when considering the amount of oil recoverable from the reservoir hence it is an important input into the recovery factor computation, material balance analysis, reservoir simulation/history match, EOR evaluations and many other applications. Laboratory techniques of obtaining oil viscosity are quite expensive and time consuming, hence the need for various mathematical correlations developed for its estimation. Majority of the correlations make use of empirical and experimental relationships developed from analyzing oil samples to obtain a trend to predict viscosity mostly for a basin. None of these has been developed for oil viscosity for Niger Delta fluids. Viscosity has been globally defined as the resistance to shear stresses within the fluid or the resistance of the fluid molecules to deformation. For a typical reservoir fluid system, where the liquid and gas exist in dynamic equilibrium, reservoir fluid composition along with temperature and pressure has been established to determine reservoir fluid viscosity1. Hence for an isothermal system and at a defined pressure in the reservoir the viscosity will be dependent on largely the composition. The reservoir fluid composition is also represented by the reservoir fluid density and the formation volume factor; therefore it is possible to deduce the viscosity of reservoir fluids from the oil density and formation volume factor even though a direct relationship has not been established between these parameters. Therefore, a correlation that can establish a relationship between the specific gravity (density) and FVF with viscosity will have significant value in the oil and industry. The data used for this analysis includes viscosity, formation volume factor, oil density at 2800 sample bubble point pressure. The data was obtained by analyzing over 3500 PVT Analysis reports, extracting the data points using a python work program, cleaning up the data and removing erroneous data, performing preliminary analysis to establish baseline relationships between the data. Supervised learning using a classification tree model was used as the machine learning approach. Seven different machine learning algorithms were reviewed, and the Random Forest Regressor was selected as the most suitable algorithm for the prediction. The model prediction results were quiet encouraging as the model was able to predict viscosity within 10% deviation from the experimental viscosity for over 80% of the cases resulting in about 90% prediction accuracy. The analysis of the results further revealed that the model could better predict viscosity of Medium to Light oil with an R2 value of between 0.90-0.96 without adjusting some obvious erroneous data points. Future of this research work will involve further in-depth analysis which will merge the preliminary QC plots with the results to evaluate the effect of the outlier sample points on the final predictability of the model. Also explore other machine learning models to further improve predictability and be able to predict viscosity across other pressure values other than the bubble point pressure to capture viscosity along the producing life of the reservoir.

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