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A curing study was undertaken to determine the effect that asphalt composition, rubber content, rubber mesh size, curing time, curing temperature, and rate of mixing have on asphalt-rubber properties. Curing temperatures of 232°C or 260°C (450°F or 500°F) and a high-shear laboratory mixer were used to produce the asphalt-rubber blends. The properties studied were rubber dissolution; rubber settling; molecular weight distribution; and low-, intermediate-, and high-temperature rheological properties. Increasing the curing temperature from 232°C to 260°C (450°F to 500°F) drastically increased the rate of devulcanization and depolymerization of the rubber, whereas increasing the rate of mixing from 4,000 rpm to 8,000 rpm drastically decreased the settling rate of rubber in a binder. Lower-molecular-weight asphalts were better at devulcanizing the rubber; higher-molecular-weight asphalts were better at depolymerizing the rubber. These high-cure binders are homogeneous in appearance and slow to phase separate on standing; they have acceptable compaction viscosities at hot-mix temperatures, higher G*/sinδ at rutting temperatures, and lower stiffness at cold temperatures than does the base asphalt.

Model calibration or history matching has commonly been conducted on a single deterministic model by a "manual" trialand- error approach. With recent advances in the application of design of experiments for using numerical models in probabilistic forecasting, the feasibility of the manual history match process used for a single deterministic model has become questionable. In manual history matching, a structured approach is used in which the sequence of adjustments has been from global, then to flow units, followed by local changes in model properties. However, the applicability of this approach to probabilistic history matching has not been demonstrated. Moreover, the industry's lack of experience in using assisted history-matching tools and the careless application without using a structured logic that is based on engineering judgment can lead the users to several potential pitfalls resulting in unrealistic solutions. This work shows that in using evolutionary algorithms, a structured (staged) assisted history-matching methodology can be applied by considering probabilistic ranges of relevant input parameters and tailored objective functions for each stage of the process. The key "heavy-hitter" parameters with the highest impact on the history-match process are identified and introduced through conducting sensitivity runs. A physically-sound and proper set of parameters with realistic ranges are introduced at each stage of the history-match process in a logical order. This paper shows that a workflow can be designed so that the ranges of selected parameters that are used to attain the best solutions at each stage can be carried over to the next stage to continue the history-match process. Since this workflow is conducted in a probabilistic semi-automatic manner, a diverse set of solutions can be obtained with the flexibility to guide the process as is traditionally done in manual history matching. The workflow is demonstrated through its application to history match production data from the Tengiz super-giant carbonate oil field located on the shores of the Caspian Sea in the Republic of Kazakhstan. Introduction Numerical models are frequently used to predict the range of ultimate recoveries and appraise different development scenarios for oil and gas assets. The validity of using reservoir models for performance prediction in brownfield assets is examined by their calibration against the historical production data. History-Match Methodologies. Model calibration or history matching has commonly been conducted on a single deterministic model by a tedious and time consuming manual trial-and-error approach, changing regional and local reservoir properties to reconcile the model with observed production data. In manual history matching, a structured approach is widely used where the sequence of scales of adjustments has been from global, then to flow units (regional), followed by local changes in model properties.