Most of the existing correlations for estimating gas viscosity were developed in mid 60's and 70's of the last century. Limited number of data was used to develop them and their accuracies are questionable. Predicting accurate gas viscosity is extremely important in the oil and gas industry as it has a major impact on reservoir recovery, fluid flow, deliverability, and well storage.In this study, a new correlation has been introduced. This correlation is simpler, features higher accuracy, and uses fewer coefficients compared with the existing correlations. Its application covers a wider range of gas specific gravity without jeopardizing the accuracy of the correlation. Another model was built using Artificial Neural Networks, ANN in order to compare its results with those obtained from the new correlation.The existing correlations were studied and analyzed using the same, large set of measured data used for this study. Most of these correlations suffered from high errors and thus were optimized using the linear and non-linear regressions. New set of coefficients for these correlations are recalculated for which the accuracy has significantly improved. In spite of such an improvement, the new correlation and new ANN model outperform the existing correlations. Litrature ReviewMany correlations have been proposed for gas viscosity estimation. These methods include Carr, et al., Jossi, et al, which was adapted by Lohernz, Bray and Clark, Dien and Stiel, Lee, et al. and Sutton. Carr, et al. 1 correlation has been very popular for estimating gas viscosity. Lee, et al. correlation has been used widely since mid 80s as it has proven to be more accurate. Each of these correlations will be discussed briefly below. The corresponding viscosity correlations are shown in Appendix A.
The South Rub Al-Khali Company Limited (SRAK) is an incorporated Joint Venture between Shell and Saudi Aramco to explore for non-associated gas and associated liquids in the South Rub'Al-Khali Basin in the Kingdom of Saudi Arabia. SRAK's contract area includes substantial volumes of ultra-sour gas in structures known collectively as Kidan. These were discovered in the 1960's by Saudi Aramco and have been appraised with several wells since then but not developed thusfar. Alongside the exploration campaign targeting the deeper Paleozoic play, the venture has carried out substantial data gathering and evaluation of the overlying Jurassic sour gas contained in the Kidan structures (Figure 1). To enable the appropriate and most cost-effective development to be selected requires a rigorous approach to project planning with focus on up-front understanding of the complexities of the reservoir. Extensive data gathering at the front end stage of the project has therefore been a key part of SRAK's strategy resulting in high quality datasets from two wells located on the Kidan North structure and a well on the Kidan South structure have been acquired. These data include core, openhole logs, fluid samples and production test data.
Specific data acquisition strategies have been developed to address the challenges of deep wells in tight reservoirs in remote and harsh environments. Advanced Mud Logging technologies have helped SRAK to gain trust in mud logging data sets to such an extent that this technology is now the prime source of information for certain datasets. Comparison of mass spectrometer derived mud gas data with downhole sampled gas has shown a very good compositional match.
Characterizing carbonate reservoirs is challenging due to their inherent complex nature, while diagenetic overprint causes an added layer of complexity. Diagenesis may enhance or deteriorate reservoir properties resulting in a wide range of pertinent rock types. The studied reservoirs consist of several shallowing upward cycles, which are capped by tidal flats lithofacies and evaporites, governing the spatial and temporal distribution of the reservoir units. In this work, we have used existing data to link sedimentology, stratigraphy, diagenesis, and wireline logs to build a full-field characterization model for improved well performance and prediction. The state-of-the-art characterization workflow includes data from sedimentologic core characterization, petrographic and diagenetic analyses, routine core analysis (RCA) at multiple overburdens, mercury injection capillary pressure (MICP) measurements, and well wireline logs. The process starts with basic facies description for all cored wells, establishing the stratigraphic framework, and then characterizing the impact of the diagenetic overprint on depositional lithofacies. Using wireline logs, the altered depositional lithofacies by diagenesis are distributed within each mapped cycle of the established stratigraphic framework and then are used to generate/predict petrophysical rock types. The final detailed and obtained reservoir property information for all the facies is used in the 3D reservoir model. This work shows and explains how facies and diagenesis led to the development of low resistivity pay positively affecting a specific reservoir. This low resistivity interval, which is not common in carbonate, is related to the presence of abundant paramagnetic minerals in the reservoir. The dispersed conductive minerals have more effect on resistivity behavior as they will induce conductivity due to their distribution and mixing with irreducible water. Variation of depositional facies has a major influence on the flow geometry. We considered these variations while generating permeability modeling as well as averaging methods, which had been used later in both static and dynamic modeling. Therefore, our petrophysical rock typing and permeability and saturation height modeling were linked to both depositional and diagenetic processes. Establishing the lithofacies relationship to flow geometry will improve the static to dynamic match, in-fill drilling plan, as well as the well design (vertical/slanted/horizontal) for both producers and injectors.
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