TRANSLATING CRUDE OIL FOULING TESTING RIG DATA TO THE FIELD : A ROAD MAP FOR FUTURE RESEARCH

To obtain fundamental and applied knowledge of crude oil fouling, numerous researchers have conducted experiments in various fouling test rigs to study fouling in a controlled scientific manner. An ideal goal for applied research is to use data obtained from test rigs to accurately predict fouling in operating heat exchangers and associated networks. Despite significant efforts to design fouling equipment that better mimics the preheat train, direct translation of test rig data to the field is yet to be achieved. Successful translation of fouling rig data to a field heat exchanger requires accounting both the impact of operating conditions and the context of the results obtained from the test rig. Although researchers have investigated many of the operating conditions and the nature of fouling data obtained from test rigs, which affect translation of fouling data, this manuscript revisits these fundamentals to identify avenues of research which are unanswered. This paper organizes and discusses the key factors that must be understood and modeled to be able to translate test rig data to the field. The gaps in knowledge outline a roadmap for future research. INTRODUCTION The complexity of crude oil fouling has resulted in many types of fouling test rigs in different research groups (Kolaczkowski, 1977; Eaton and Lux, 1984; Asomaning, 1990; Shetty et al., 2016; Tajudin et al., 2015; Smith, 2013; Crittenden et al., 2015; Joshi, 2013). Currently, results of crude oil fouling experiments provide insights on fundamental behaviors and on the impact of different surfaces and crude types (e.g., Hazelton et al., 2015). They aid in the identification of dominant fouling mechanisms and help in evaluation of the efficacy of mitigation strategies (e.g., the use of antifoulants or coatings). They also provide useful comparative results (e.g., Crude B has an initial fouling rate twice that of Crude A), as well as evaluation of fouling of crude blends including crude incompatibility. Although these applications are valuable, direct quantitative translation of the fouling rig data to the plant is not widely practiced. Translating fouling rig data to the field and quantitatively using fluid chemistry to predict crude oil fouling are two long-standing goals of crude oil fouling research. Refiners especially want to know how a crude (or blend) will foul and assess the fouling costs associated with operation of the preheat train, as well as costs associated with cleaning and modifications. These costs would then inform the crude price and purchasing decisions. This paper looks at the differences between fouling rigs and preheat train exchangers and lays out the key factors that are required to be addressed to successfully translate test rig data to the field. From this, the fouling community can assess the gap in achieving this goal and the research required. What we want to map Translating rig data to the field requires models that account for the impact that operating conditions and the test rig have on fouling. As illustrated in Fig. 1, 1. Models need to fit fouling test rig data. 2. The translation process must update the models for their application to the crude preheat train (PHT) context. Fig. 1 Illustration of how test rig results would be used to predict refinery fouling. Oil Test conditions Test rig for a specific