February 2022

Special Focus: Digital Technologies

Web-based software for predicting crude compatibility and optimization for increasing heavy oil processing

Refineries in oil-importing nations typically process a blend of crude oils, rather than a single crude oil, to ensure that an optimum product mix can be obtained at the minimum costs.

Kumar, R., Rakshit, P. K., John, M., Voolapalli, R. K., Bharat Petroleum Corp. Ltd.

Refineries in oil-importing nations typically process a blend of crude oils, rather than a single crude oil, to ensure that an optimum product mix can be obtained at the minimum costs. To increase margins, refineries are looking for ways to co-process heavy crude oils with light crude oils.1–4 Heavy crude oils contain high amounts of paraffins or asphaltenes. High paraffin content results in high viscosity and high pour point, making transportation difficult. Conversely, high asphaltene content causes precipitation, flocculation, instability and incompatibility challenges during processing. This severely affects process equipment like heat exchangers, pumps and tanks.5–8

The current benchmark process to determine compatibility of blending two crude oils consists of 9–10 standard laboratory-based test methods, out of which 3–4 are required to be done as per their applicability range. These tests can take weeks to complete. At present, there is no standard practice to check the compatibility parameter in advance; rather, chemical dosing is done to prevent incompatibility-related problems in refineries. Therefore, increasing co-processing of heavy oil components is a real challenge.

To increase the heavy oil content in the mix of crude oils, and for suitable oil selection for co-processing, refiners encounter several common problems on day-to-day operations. These include:

  • Incompatibility/stability issues when the crude oils are blended
  • High viscosity of the blend
  • High pour point of the blend
  • High sulfur content in the blend
  • High acidity and nitrogen content
  • Low distillate yields and availability for feedstock choices.

The objective of this article is to provide a quick and effective method for predicting crude oil blend compatibility, as well as for optimizing heavy oil processing, using a prediction model softwarea. The prediction model is based on the measurement of a few bulk physical parameters, which are conventionally and regularly analyzed in a refinery’s quality-control laboratory. This analysis typically takes less than 1 hr, with no additional tests required, which enables refiners to quickly make blending decisions.

In contrast to conventional methods, the present subject matter does not require comprehensive laboratory testing for compatibility and blending, which otherwise normally takes several weeks. Using the prediction model softwarea, operators can increase heavy oil processing and substantially eliminate operational problems related to asphaltene precipitation caused by crude blend incompatibility. Furthermore, conventional laboratory test methods can optimize the blending of only two crude oils at a time. To optimize a blend of three crude oils, a compatible blend test of two crude oils must be obtained, followed by a compatibility test of the first two blended crude oils with the third. If additional crude oils need to be blended, then the compatibility checking and blend optimization becomes even more complicated.2

Therefore, another objective of this article is to devise a methodology for compatibility prediction and for the optimization of blends having any number of crude oils. The focus of this work can be used for increasing heavy oil processing and can help eliminate problems caused by crude oil incompatibility.


Asphaltene precipitation has been a common problem in refineries. It occurs due to incompatibility of the crude mix, especially when the heavy oil fraction increases in the blend. The quick and reliable prediction of crude oil blending compatibility is critical for the best selection of crude oil blends.

The compatibility of crude oil blends can be estimated using the following tests: the colloidal instability index (CII), the colloidal stability index (CSI), the Stankiewicz plot (SP), qualitative-quantitative analysis (QQA), the stability cross plot (SCP), the Heithaus parameter (or parameter P), heptane dilution (HD)/toluene equivalence (TE), the spot test and the oil compatibility model (OCM), among others.9–13 All these experimental methods are based on the physical model of asphaltenes and their solubility with other components present in the oil system. The typical physical model has been depicted in FIG. 1. These experiments can take a long time to conduct—1 wk per blend of two crude oils at a time—and are, therefore, tedious.

FIG. 1. Physical model of asphaltene precipitation.

Proprietary prediction model softwarea

A novel tool for predicting the compatibility of crude oil blends and blend optimization for increasing heavy crude oil processing has been developed. This model uses the four physical parameters of crude oils—sulfur, carbon residue, API and kinematic viscosity—as input for blending optimization.14–23

The model is developed by coefficients obtained via regression analysis between the ratios of the physical parameters of known crude oils, and by the composite compatibility measures determined from multiple compatibility test results of the known crude oils. The model’s equation is provided in Eq. 1. This effectively correlates characteristics of asphaltene molecules and their behavior. The content of aliphatic carbon attached to the aromatic core of asphaltene in a heavy crude oil is the primary deciding factor for determining crude blend compatibility when blending with light crude oil. Higher aliphatic carbon attached to the aromatic core of asphaltene in heavy crude imparts instability when blending with light crude oils.

K = k1 × (1/C) + k2 × (C/A) + k3 × (C/S) + k4 × (log(S)/C) + k5 × (A/S) + k6 × (S/V) + k7 × (V/A) + k8 × (V/C)   (1)

where k1–k8 are the coefficients of regression. When K ≥ 0, the crude oils are compatible; when K < 0, they are incompatible.

The proprietary prediction softwarea accurately predicts the composite results of all comprehensive laboratory test methods within a few minutes. The prediction software enables refiners to predict compatibility of multiple crude oils (up to 10) within a short amount of time.

The parameters for blend optimization are blend compatibility, blend viscosity,24 blend pour,25 blend acidity,19 blend sulfur, blend nitrogen19 and total distillate yield.19 The optimization module also considers crude oils/tank storage availability (FIG. 2).

FIG. 2. Optimization module in the prediction model softwarea.


Seventy different crude oils have been used for the development of the model prediction software, while 16 neat crude oils and 14 crude oil blends have been used for validation (TABLES 1–2). The K value was determined based on the compatibility model in Eq. 1 for 16 crude oils. The results of the compatibility parameter K were compared with the compatibility results based on saturate, aromatic, resin and asphaltene (SARA) analysis and spot tests.

It is known that any single test method is inadequate to make accurate decisions regarding the compatibility of crude oils and blends. In this case, individual and composite results of all known laboratory test methods have been considered to validate the K model. In addition, some of the 16 crude oils were also processed in a refinery, and the observation of their compatibility/stability during processing is provided in TABLE 1, where applicable.

Among all predictions of six different SARA-based methods, the SCP and SP methods were observed to be closer with the K model prediction. The maximum deviations of the K model results were observed vs. the CII method. Validations of different methods (SCP, spot tests and OCM) are shown in FIGS. 3–5. It was observed that all 14 compatibility blends predicted by the prediction model softwarea were in line with the SCP charts (FIG. 3). The accuracy of the K model was ±1 wt%.

FIG. 3. Validation of the K model with SCP method.
FIG. 4. Comparison of K model, OCM and spot test methods of crude oil blend compatibility.
FIG. 5. The K model predicting tests of neat crude oils and blends.

The prediction softwarea determinations of the regions of compatibility/incompatibility for the Ratawi/Saharan blend and the Ras Gharib/Saharan blend were compared with the OCM and spot test methods. The results were a very close match, as depicted in FIG. 4. Furthermore, the K model is also able to predict the shades of light to heavy crude oils, as the color-intensity shades correspond to variations of the K value from low to high.

According to the K value, the intensity of the spot test color shade changes, along with the type of crude oil (light and heavy). Higher K values are shown as dark colors, with lower K values shown as light colors (FIG. 5A).

To further validate the predictability, blends were prepared with a K value of 0, and the spot tests were observed to be similar in shades, as depicted in FIG. 5B. This is the level of accuracy displayed by the K model to control asphaltene flocculation to precipitation behavior.

Prediction softwarea advantages

There is no standard practice to check the compatibility parameter in advance; rather, excess antifoulant/chemical dosing is done to prevent incompatibility issues. This is because laboratory-based methods are time consuming. The advantages of the proprietary predicting software are provided in TABLE 3.

Impact on refinery operations (equipment, energy and environment)

There is a strong relationship between the K model and with the intensity of spot color, desalting performance and fouling behavior, which was further verified through experiments. If the K value is positive, then the spot color is darker, and, additionally, desalting is better and fouling is at a minimum. However, if the K value is negative, there is a lighter spot color (with an indication of asphaltene flocculation or precipitation), along with poor desalting and high fouling.

Desalting is a water-washing operation performed to remove salts prior to the crude distillation column. Salt and water content specifications are more stringent because of their negative effect on downstream processes (e.g., corrosion and catalyst deactivation). Incompatible crude oil blends are expected to be problematic for water separation due to asphaltene precipitation, which causes stable oil-water emulsion formation.10,26

In the present study, the K model-predicted Incompatible Blend A (Ras Gharib 35 wt% and Saharan Blend 65 wt%, with a K value < 0) and Compatible Blend B (Ras Gharib 65 wt% and Saharan Blend 35 wt%, with a K value > 0) were subjected to desalting experiments. Dehydration efficiency was calculated as the ratio of separated water to the added water after 5 min and 10 min. The dehydration efficiency (water separation) of Compatible Blend B is estimated as 37.78 wt% and 41.12 wt% for 5 min and 10 min, respectively; however, there was no water separation observed with incompatible blend A.

The precipitation or deposition of asphaltenes from incompatible crude oil blends is expected during processing.10,18 In this study, K model-predicted Incompatible Blend A (Ras Gharib 35 wt% and Saharan Blend 65 wt%, with a K value < 0) and Compatible Blend B (Ras Gharib 65 wt% and Saharan Blend 35 wt%, with a K value > 0) were subjected to fouling thermal experiments. In these experiments, the temperature drop profile was measured against duration (FIG. 6). The temperature drop of the compatible blends is approximately 34°C; however, the incompatible blends had a high temperature drop of about 59°C. This showed that deposition in Incompatible Blend A is higher vs. Compatible Blend B over the surface of the heating rod; the heat transfer rate reduced significantly, as well. This experimental result is the evidence of a strong relationship between the K model and the fouling profile. Therefore, the optimization of blends using the K model can be further used for optimizing anti-fouling chemical dosing for fouling mitigation.

FIG. 6. Fouling profile of compatible vs. incompatible crude oil blends.

The K model is expected to improve refinery operations for the desalting and fouling of heat exchangers, which, in turn, helps keep refining equipment in good working order. Asphaltene precipitation typically puts an extra load on the preheat trains (PHTs), which causes a 1°C–3°C decrease in temperature. For context, in a nearly 96,000-bpd refinery, a 1°C improvement in the heat exchanger network (HEN) of the crude distillation unit equals approximately $1 MM/yr. In addition, to compensate for this, extra fuel firing is required, which typically generates 90,000 tpy of CO2/°C in the HEN. The implication of asphaltene precipitation on the desalting performance—which is evident from incompatible blends B3, B7 and B8, where there is no water separation—leads to heat exchanger fouling in the downstream processing system and impacts environmental health, as depicted in FIG. 7. The adherence to the proprietary prediction softwarea can lead to a significant increase in fuel savings and to a reduction in CO2 emissions.

FIG. 7. Implications of crude incompatibility.


Crude oil cost constitutes 85%–90% of a refinery’s input costs. For example, if a 350,000-bpd refinery processes crude oil that costs $1 less, that equates to $40 MM–$45 MM in additional profit.27 While increasing heavy oil processing, incompatibility issues are inevitable. Therefore, the quick and reliable prediction of crude blend compatibility is critical to maintain the good health of refinery equipment and operations.

More than 2,000 crude combinations—involving approximately 200 different crude oils—were analyzed for processing heavy/opportunity crudes in Bharat Petroleum Corp. Ltd.’s (BPCL’s) refineries. The prediction softwarea was used to make quick decisions on feeding a compatible crude mix to maintain the health of refining equipment, while meeting fuels demand. Note: It only took 4 wk–6 wk to analyze 2,000 crude mixes, while laboratory studies would have required several months.

Case study: BPCL’s Mumbai refinery

While co-processing a new crude oil at BPCL’s Mumbai refinery, the technologists deviated from the prediction softwarea, which caused an upset in desalter operations (amperes increased for a longer period due to asphaltene precipitation, and strong emulsion formed with water). To correct this action, technologists used the prediction software to optimize the crude oil blend composition. After 2 hr–4 hr, the desalter was brought back to normal operation. Thereafter, the Mumbai Refinery strongly recommended that the prediction software should be included in the pre-processing plan, so that crude oil blends could be finalized. Since this episode, the prediction software is used regularly in all BPCL refineries.

The prediction software also helped crude-sourcing teams identify which crude oils are best suited for processing and assisted in regular monitoring of asphaltene flocculation to precipitation behavior in refinery operations. The prediction of asphaltene flocculation to precipitation behavior is depicted in FIG. 8.

FIG. 8. The prediction softwarea accurately predicts asphaltene precipitation behavior.

Additionally, the prediction softwarea model predicts the compatibility of intermediate streams within various refining units. This unique feature can predict incompatibility hotspots in various unit operations employed in refineries. The prediction software’s solutions have been used by several refinery units to address desalter upsets, the selection of heavy crude combinations, and the compatibility ranking of crude mixes. The predictions and rankings of crude compatibility have been observed in line with field operations.

To enable increased margins, the prediction software guides operators toward enlarging their crude oil baskets, including new and opportunity crude oils for processing. In this case, for example, heavy oil was to be selected for co-processing, which the prediction software predicted that specific crudes were compatible; however, the intermediate streams—especially the vacuum residue (VR)—derived out of the crude mixing process in certain compositions were incompatible, since they could lead to severe fouling in the VR heater/furnace when handling or processing VR in the delayed coker. Based on the VR compatibility, some of the crude oils were avoided for co-processing. In certain instances, the prediction softwarea not only guides the user to a compatible composition, but also advises not to include certain compositions in the crude basket, based on the incompatibility of intermediate streams and refinery configurations. One of the selections of heavy oils—sourced from Mexico—for co-processing with 15 regular crude oils is reported in TABLE 4. In this example, the final co-processing decision was primarily influenced by VR compatibility.

Due to its ability for rapid solutions, the K model has been used for scheduling healthier feeds for the crude mix processing, including monthly planning and real-time monitoring of asphaltene precipitation behavior. The continuous usage of the prediction software resulted in an increase in refining margins of $0.15/bbl–$2/bbl vs. regular crudes.

Prediction softwarea modules and features

The prediction software package offers different modules that provide a variety of options to meet refinery blending needs. This software package has three modules, as depicted in FIG. 9. The first module is for crude oil blend compatibility and has two submodules. Module 1A is used to predict crude blend compatibility for known blend compositions. Module 1B is used to predict the most optimum and compatible crude oil blends that can be achieved using a set of crude oils. The prediction software provides an option for blend optimization of up to 10 crude oils. This option considers different blending constraints, such as blend compatibility, blend viscosity, blend pour, blend acidity, blend nitrogen, blend sulfur and total distillate yield, and crude oil availability.

FIG. 9. The prediction software’s web page displays software features.

Similarly, Module 2 is used to indicate possible bitumen within blends. Module 2 also has two submodules. Module 2A can check if a given crude oil blend composition has the potential for bitumen. Module 2B is used for estimating the optimum blend composition of crude oils with bitumen potential.

The third module is used for fuel oil blending, which enables the optimization of cutter stock and also minimizes the cost of production for a fuel oil of a certain specification.


The authors’ company’s proprietary crude compatibility softwarea provides accurate prediction of crude compatibility within minutes vs. weeks. This software has been validated with the composite results of nine different compatibility tests available in literature for accurate compatibility prediction. The ability to predict crude oil compatibility can significantly improve refining margins, especially for refiners that work with large crude mix parcels. For cases where 4–10 crudes are likely to be blended, predicting compatibility will be a necessity and not a luxury. The prediction software also guides refiners for optimal fuel oil blending and crude blending for bitumen production. Most importantly, this will assist refiners to process crude mix parcels with a greater number of constituents to increase profitability.

Refiners can use the software to improve equipment life, ensure smooth operations, increase energy savings and lessen environmental impact. This software has the capability to significantly contribute to the world if refineries adhere to minimizing asphaltene precipitation. HP


a BPCL’s K Model


The authors would like to express their sincere thanks to BPCL management for its constant support in model validation and commercialization. In addition, the authors would like to thank CRDC and refinery colleagues (Dr. Rajkumar, Ms. Sonal, Mr. Ankur, Md. Muzaffar, Mr. Prasad and Mr. Sathiyanarayanan), along with colleagues from the Indian Institute of Technology (IIT) Delhi (Professor Sreedevi Upadhyayula, Dr. Tanmoy Patra and Dr. Shashank Bahri) for research support.


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