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Dimensional analysis was performed to understand the physics of ionic dispersion in reservoir rocks and to identify the factors influencing the cation exchange capacity (CEC) of these rocks. Dimensional analysis revealed the existence of a general relation independent of the unit system between two dimensionless groups denoted as the cationic dispersion number [Formula: see text] and the conductivity number [Formula: see text]. The former group [Formula: see text] stands for the ratio of the CEC to the electrical double-layer dispersion. The latter group [Formula: see text] represents the ratio of the low-frequency ionic conductivity to the high-frequency electronic polarization. Complex dielectric permittivity measurements on 121 water-saturated sandstone and carbonate rock samples were used to validate the dimensionless groups. In retrospect, dimensional analysis was useful in identifying variables influencing the CEC of hydrocarbon rocks. In particular, these variables consist of rock porosity [Formula: see text], specific surface area, and five other parameters of the Cole-Cole function, which describes the frequency dependence of the complex permittivity of rock samples in the range 10–1300 MHz. The Cole-Cole function parameters are [Formula: see text], which is a characteristic relaxation time; [Formula: see text] is the so-called spread parameter; [Formula: see text] is the real DC conductivity of water-saturated rocks; and [Formula: see text] and [Formula: see text], which are the real numbers representing the static and the high-frequency dielectric permittivities of the water-saturated rock, respectively. A general regression neural network (GRNN) model was developed to predict the CEC of shaly sandstones and carbonate rocks as a function of the variables identified by the dimensional analysis as essential in predicting the CEC. The CEC prediction capability of the GRNN model has been tested with a blind data set, and it has been compared with the CEC prediction capability using a nonlinear regression model developed in this study and using a linear regression model available in the literature. The GRNN model outperformed both of these empirical models. With the GRNN model, it is possible to obtain reliable quantitative estimates of the CEC of shaly sandstone and carbonate rocks using nondestructive frequency-dependent dielectric permittivity measurements that are rapid, economic, and accurate. In return, accurate and fast estimates of the CEC are useful in many petroleum engineering applications. They can be used to identify clay types and can also be used to quantify the volume of hydrocarbon in shaly sands using well-log resistivity data. The results of this study represent a major advantage for formation evaluation, wellbore stability analysis, and designing stimulation jobs.
Dimensional analysis was performed to understand the physics of ionic dispersion in reservoir rocks and to identify the factors influencing the cation exchange capacity (CEC) of these rocks. Dimensional analysis revealed the existence of a general relation independent of the unit system between two dimensionless groups denoted as the cationic dispersion number [Formula: see text] and the conductivity number [Formula: see text]. The former group [Formula: see text] stands for the ratio of the CEC to the electrical double-layer dispersion. The latter group [Formula: see text] represents the ratio of the low-frequency ionic conductivity to the high-frequency electronic polarization. Complex dielectric permittivity measurements on 121 water-saturated sandstone and carbonate rock samples were used to validate the dimensionless groups. In retrospect, dimensional analysis was useful in identifying variables influencing the CEC of hydrocarbon rocks. In particular, these variables consist of rock porosity [Formula: see text], specific surface area, and five other parameters of the Cole-Cole function, which describes the frequency dependence of the complex permittivity of rock samples in the range 10–1300 MHz. The Cole-Cole function parameters are [Formula: see text], which is a characteristic relaxation time; [Formula: see text] is the so-called spread parameter; [Formula: see text] is the real DC conductivity of water-saturated rocks; and [Formula: see text] and [Formula: see text], which are the real numbers representing the static and the high-frequency dielectric permittivities of the water-saturated rock, respectively. A general regression neural network (GRNN) model was developed to predict the CEC of shaly sandstones and carbonate rocks as a function of the variables identified by the dimensional analysis as essential in predicting the CEC. The CEC prediction capability of the GRNN model has been tested with a blind data set, and it has been compared with the CEC prediction capability using a nonlinear regression model developed in this study and using a linear regression model available in the literature. The GRNN model outperformed both of these empirical models. With the GRNN model, it is possible to obtain reliable quantitative estimates of the CEC of shaly sandstone and carbonate rocks using nondestructive frequency-dependent dielectric permittivity measurements that are rapid, economic, and accurate. In return, accurate and fast estimates of the CEC are useful in many petroleum engineering applications. They can be used to identify clay types and can also be used to quantify the volume of hydrocarbon in shaly sands using well-log resistivity data. The results of this study represent a major advantage for formation evaluation, wellbore stability analysis, and designing stimulation jobs.
Objectives/Scope This paper covers the Shwe Offshore Platform (SHP) MEG Regeneration System upgrade works implemented to reduce excessive MEG losses and improve operation of the System. Methods, Procedures, Process The SHP MEG Regeneration System processes MEG used in the topsides for hydrate inhibition in the Low Temperature Separators (LTS) and at the four subsea wells. Since start-up of production in 2013, SHP has suffered from excessive MEG losses and operational difficulties. Later in the field life, MEG injection to subsea wells was discontinued due to contamination with salts from formation water, and Kinetic Hydrate Inhibitor (KHI) injection was introduced. Extensive evaluation of data, laboratory tests and field trials were carried out to investigate the causes of the excessive MEG consumption and operational difficulties. The upgrade of the MEG Regeneration System was finally completed and put in service since May 2018. Results, Observations, Conclusions The followings have been identified as the direct causes for MEG losses and operational difficulties; Poor efficiency of Glycol-Glycol Heat Exchangers (HEXs), causing low operating temperature of the Rich MEG Flash Drum and poor condensate/Rich MEG separation, resulting in MEG losses via Flash Drum condensate outlet.Excessive condensate in the Rich MEG reaching the Reboiler and resulting in contamination of the Lean MEG and downstream section (injection spray bars).Low water loading in the MEG Regeneration System resulting in lack of reflux and MEG losses via Reflux Drum.MEG carryover from LTS to the Export Gas Compressor (EGC) Suction Scrubbers, caused by foaming in the LTSs. In order to resolve the issues above, a number of modifications were implemented. New Glycol-Glycol HEX and a Liquid-Liquid Coalescer Filter were installed to provide better separation of condensate from Rich MEG. Also re-routing of the EGC Suction Scrubbers liquid outlet back to the MEG Regeneration System to recover the carried over MEG. Significant reduction of MEG losses was achieved, in the range of 70%, and overall system performance has improved. The main outcome of such improvement is that the condensate content in Rich MEG has been significantly decreased to below 500ppmV from above 1vol% due to the increase of Rich MEG Operating Temperature in Flash Drum and introduction of the Liquid-Liquid Coalescer Filter. Novel/Additive Information This paper was written to describe the method used for resolving the problems with MEG losses, excessive condensate in the Rich MEG, and MEG carryover. It also explains the data evaluation, preparation, execution, and the results of the MEG Regeneration System Upgrade work.
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