Method and system for assessing the performance of crude oils

ES 2 380 720 T3

DESCRIPTION

Procedure and system for evaluating the performance of crude oils

Field of the invention

The present invention relates to crude oil processing, and more particularly to a method and system for predicting the performance of crude oils and crude oil blends. Specifically, the present process provides a means to allow petroleum refineries to process crude oil blends that include less optimal oils, while reducing the risks involved due to fouling of operating equipment, particularly heat exchange networks, thus operating. the oil refinery in more economical and environmentally beneficial ways.

Background of the invention

Oil refineries are under intense pressure to process lower quality crudes because of price or availability, or both. Unfortunately, in many cases, oil refineries do not have enough information and knowledge about some crude oils or blends of crude oils and about how to behave in an operating environment to make the processing of these crude oils viable. Individual refineries only have access to information and knowledge of the crudes that they have tested and used effectively,

In an effort to solve the problem of not having information about some crudes and how they behave in an operating environment, some refineries have started using laboratory simulations to develop predictive models of some yields. These models, however, are limited and do not solve specific often complex problems that can arise during the processing of these crude oils and how these problems can be alleviated using appropriate chemical treatment solutions. US Patent 5,412,581 discloses and claims a method for predicting and evaluating the physical properties of hydrocarbons using spectrometry.

Linear scheduling systems have also been applied that focus on defining the crude cut and corresponding crude yield, but these systems do not address the use of treatment chemicals in the crude selection mode, nor do they evaluate the amount of fouling. of equipment during processing to allow a risk assessment of cheaper crudes. These procedures cannot tell refiners how crude blends will affect operations and equipment. Thus, refineries lack the critical information they need to access the risk and economic feasibility of using substandard crudes.

Crude preheating train fouling is not a well understood phenomenon. Refiners often do not have enough information to determine whether or not the use of a new crude or blend of crude oils is fouling the refinery equipment, particularly the heat exchange networks. Clogging of the heat exchange network can result in a rapid drop in temperature at the furnace inlet and lead to significant economic and environmental consequences. Although an appropriate chemical treatment can extend the life of heat exchangers, the lack of quantitative understanding of the phenomenon makes the proposal of a treatment more complex. Therefore, it is desirable to have the ability to quantify the fouling propensity of a particular crude being processed and its impact on operating conditions.

Consequently, there is a need for a means of assessing and evaluating the selection of crude oils, and predicting the cost and risk associated with them.

More particularly, there is a need to determine the optimal dosage of crude oils with treatment chemicals so that crude blends that include cheaper crude oils can be used without deleterious effects on refinery operating equipment, such as fouling of refineries. heat exchange networks.

More particularly, what is needed is a methodology and a system that will allow refineries to process cheaper or substandard crudes, or blends comprising substandard crudes, using models to predict the fouling propensity of such crudes. The methodology should allow refineries to reap the benefits of using cheaper crudes, lower costs on potential treatment chemicals, increase production, extend the production cycle and reduce operating costs all in a more environmentally friendly environment.

Document EP0241233 discloses a method for determining the fouling tendency of hydrocarbons. A relationship is determined and compared to a relationship graph.

Summary of the invention

The present invention provides a system for evaluating the risk of processing crude or blends of crude from

ES 2 380 720 T3 inferior quality in refinery operations, said risk including the propensity for fouling of crude or blends of crude oils, said system comprising: a database that stores data relating to at least one crude or a blend of crude oils; and a predictive engine comprising a fouling propensity model for executing at least one prediction of the fouling propensity of a heat exchanger in a heat exchange network, characterized in that the predictive engine takes as input crude information at least one of the surface or skin temperature and the velocity of the oil through the heat exchanger, and at least one of the mass transfer characteristics, reactive nature of the fouling species in the crude oil and the mass surface temperature.

The invention also provides a method according to claim 5.

The embodiments of the presently claimed invention allow refiners to process higher percentages of lower grade crudes by recommending optimal mixing ratios and optimal process conditions, particularly surface temperature and crudes rate. Likewise, any future reduction in the percent cleanliness of each heat exchanger in the network is quantified. Once in place of this information, refiners can determine the required chemical treatment determined by quantifying the impact of various chemicals, allowing the selection of the best possible treatment to mitigate the degradation of performance of the heat exchange network, quantify the improvement in performance through appropriate chemistry and optimizing the dosing of treatment chemicals based on the crude being processed.

Brief description of the drawings

Figure 1 is a block diagram of an illustrative embodiment of a system for recommending optimal crude blends and chemical treatments of these blends.

Figure 2 is a block diagram of an illustrative embodiment of a crude oil pretreatment train fouling prediction structure.

Figure 3 is a graph showing an estimate of parameters.

Detailed description of the invention

The singular forms "a" "an" and "the" or "the" include plural referents unless the context clearly indicates otherwise.

The modifier "about" used in conjunction with a quantity is inclusive of the stated value and has the meaning dictated by the context "for example, includes the degree of error associated with the measurement of the particular quantity).

"Optional" or "optionally" means that the event or circumstance described below may or may not occur, or that the identifying material below may or may not be present, and that the description includes instances where the event or circumstance is produces or in which the material is present, and cases in which the event or circumstance does not occur or the material is not present.

Crudes and blends of crude oils are used interchangeably and each is intended to include both single crude and blends of crude oils.

It teaches a procedure and system for evaluating crude oils and crude blends, specifically characterizing the impact of various constituents of crude oils on fouling of heat exchangers and recommending optimal chemical treatments to minimize fouling and thereby reduce the rate of reduction of the furnace inlet temperature. The system and method of the invention are described herein with reference to Figures 1 and 2. With reference to Figure 1, a block diagram of an embodiment of the system for detecting crude oil parameters, predicting the performance of the heat exchange network and propose the chemical treatment generally designated by the reference number 100. System 100 comprises a proprietary database 106, more particularly known as a hot liquid process simulator ("HLPS") that stores a massive amount of data, including experimental data, relative to different types of crude 102, their characterizations, operating and refinery conditions in which crudes were processed along with any processing difficulties and / or performance or risk parameters, and laboratory simulation data. The method and the system use the data as the basis for at least one predictive performance model and / or at least one risk assessment model aimed at optimizing the mix composition, chemical treatment and / or operating conditions 120 of the network. heat exchange.

The information contained in the database includes information on crude oil 102, which was obtained as shown in figure 2. The information detected for each crude oil 202 is provided, along with the particular crude mixture 201, at which point a Determining whether or not the mix is ​​compatible, the information then advances to the database. If the determination is no, then it is suggested not to use crude 205. This crude information 102 is then stored in the HLPS database 106. They can be performed

ES 2 380 720 T3 laboratory scale studies to determine particular parameters for individual crude oils. The properties are then estimated for the mixture having different crudes in known quantities. The parameters itself extracted are then corrected for specific refinery operating conditions and used in the fouling rate predictive engine 108 at a later stage.

A stage in the methodology comprises the means to identify and detect parameters of the fouling propensity model 110. This stage mainly seeks to detect the fouling propensity of individual crude samples, which will then be used to predict the fouling tendency 112 of the crude blends incorporating said particular crude. Two operating conditions that impact fouling rate are skin or surface temperature and oil velocity through heat exchangers. Additional parameters that can be optimally considered at this stage are the mass transfer characteristics, the reactive nature of fouling species in the crude oil, and the bulk surface temperature.

Crude data can also be used to define and recommend compatible mix ratios, as well as optimal mixes dependent on the operating conditions of a particular refinery.

Also included in the system 100 is the predictive engine 108 used to predict the performance of the heat exchanger network. Figure 2 shows a block diagram representing an embodiment of the overall system 200 for the fouling prediction structure of the crude preheating train. As mentioned above, the oil information 102 for a particular oil or oil blend is entered to determine its compatibility. If compatibility is found, then the crude information 102 advances to the HLPS database 106. The information will then be used to predict the performance of individual exchangers by subjecting the data to fouling propensity model 110, which will evaluate the fouling propensity of each exchanger in network 112 and the exchangers will be rated. They will be indicated as a) exchangers that will have accelerated fouling; b) exchangers that could be marginally at risk of accelerated fouling; and c) exchangers that will not be significantly affected .

The predictive engine 108 can then use the compiled information to determine the future fouling trend for each of the heat exchangers and then for the entire heat exchange network. The key parameters used in the model are the diffusion coefficient, the fouling concentration, the reaction rate constant, and the activation energy. This information is compiled and provided, along with additional data, such as refinery 101 conditions. In this way, this structure provides the prediction of the furnace inlet temperature as a function of the crude or mixture of crude oils being processed.

Likewise, the crude oil information and predictive engine 108 can be used to define optimal processing conditions, namely surface temperature and crude velocity, thereby reducing the fouling ratio of individual heat exchangers in the process. network.

The third stage or level in this procedure or system is the choice of operating conditions and the application of chemical treatment to optimize crude processing and minimize the risk of fouling of heat exchangers and the global network. Taking into account the crude oil information and fouling prediction as determined in the previous stages or levels, the refinery can determine the optimal chemical treatment dosage, and performance parameters 120. This is done by quantifying the impact of different chemicals on crude oils and their fouling potential, and allowing the selection of the best possible treatment to mitigate performance degradation of the heat exchange network. Therefore, the improvement in performance through appropriate chemistry is quantified, and the dosing of chemicals is optimized depending on the crude being processed.

Finally, the model allows the refinery to quantify savings with and without crude treatment. By evaluating different crude oils and crude blends, this problem provides a risk assessment of the use of cheaper crude oils. The optimal dosage of chemicals can be predetermined and therefore their corresponding cost is known before using the crude oils, since it is a harmful or fouling effect of the crude on the heat exchange network. Allowing in this way the risk assessment regarding the fouling of the network and any corresponding shortening of the life of the equipment.

Example

A laboratory scale experiment was carried out to circulate crude oil and crude blends through an electrically heated exchanger. A first detailed fouling model principle was formulated for laboratory scale studies. An analysis was performed to run the experiments to capture the effect of temperature, composition and impact of the chemicals. See the following Table I. The experiments were run for different crudes and blends of white crudes at different surface temperatures. The experiments were then repeated with an addition of chemicals to the same soft crudes. Soiling model parameters were extracted for run cycles. A first principle based on the fouling ratio model was formulated for an extended heat exchanger that

ES 2 380 720 T3 involved the shear effect due to turbulence. The parameters derived above were used in the fouling ratio model for one of the beta sites where the exchangers were classified as high risk, medium risk and low risk exchangers. Details are given in Figure 1A.

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