Many development wells do not have sonic logs recorded, often because of mechanical issues with deviated wellbores or high cost. Consequently, tying development wells to the seismic data covering the field becomes difficult. This issue is magnified in fields where multiple heterogeneous thin sands form thick-stacked pay packages. Multivariate linear regression is a powerful tool to analyze the interdependence of data. Well data from three producing fields in the Balingian Province, offshore Sarawak, Malaysia, are used to calculate parameters relating the recorded sonic-log data to other recorded petrophysical log data. Those parameters are used next to estimate sonic logs from petrophysical log data alone. The petrophysical log data include depth, gamma ray, density, neutron porosity, and resistivity, thus reflecting the natural assumption that the formation velocity is dependent on compaction, lithology, density, pore space, and fluid content. Parameters are calculated separately for coals, gas-filled sands, and the normal shale and sand sequences, giving one set of parameters for each well. The regression is computed at log scale for every depth point. The coefficient of determination between recorded and estimated sonic logs for the same well is up to 0.96. Blind testing is applied to assess the actual reliability of the linear regression by using the parameters from each well in turn to estimate sonic logs for the other wells with only their petrophysical logs. The best set of parameters is obtained from composite wells with tens of thousands of depth points, where the data from several wells are combined. This ensures that there are multiple instances of coal layers and gas-filled sand layers at many depths, thus providing the most representative data set. Interpretation indicates that the synthetic seismic from estimated sonic logs leads to reliable observations regarding sands and coals and their seismic character.
Estimating oil and gas reserves is one of the most important functions for petroleum companies to support portfolio management and revenue forecasting. The investment com-munity uses reported reserves to assign values to companies or to individual projects, which is important to the stock markets and for financing projects. Governments use reported reserves for regulatory oversight and for forecasting national petroleum production. In 2008, one of those government agencies, the U.S. Securities and Exchange Commission (SEC), published “Modernization of Oil and Gas Reporting.” The document was based on numerous recommendations to update reporting rules for oil and gas companies to reflect the advance in technologies.
The Petroleum Resources Management System (PRMS) is designed to provide consistency in estimating natural occurring petroleum quantities, evaluating projects to commercially extract and market derived products, and present results within a comprehensive classification framework. PRMS is the latest result of international efforts to standardize the definitions of petroleum resources and how they are estimated that began in the 1930's.
A comprehensive seismic interpretation programme was recently initiated with the purpose of further increasing the use of 3D seismic data for reservoir characterisation and field development in the Al Shaheen Field, offshore Qatar. Reprocessing of existing seismic data was part of the programme to ensure best data quality for the interpretation. The Al Shaheen Field is a layered carbonate dominated field with multiple reservoirs at different stages of development. Reservoir characterisation is a key driver for both new development areas in the field and for optimisation of existing development areas. Geological topics relevant to reservoir characterisation and field development where seismic data support the reservoir models include faults, reservoir architecture and properties. In this study we present results of integrated seismic interpretations aiming at improving the reservoir characterisation. The results span three of the important reservoirs: Kharaib, Shuaiba, and Mauddud. Through seismic interpretation and integration with geological data and concepts, a consistent field-wide fault framework has been defined. A complex channel system in the Shuaiba reservoir and clinoforms in Mauddud have been mapped. The porosity distribution in the Kharaib reservoir has been estimated using seismic attributes. Additionally all the main geological surfaces defining the general structure and stratigraphy have been mapped. For some results the confidence is high and they can be used directly in the reservoir characterisation and building of static and dynamic models. Other results are less certain e.g. porosity of the Kharaib reservoir. Where this is the case, the results are used to help define uncertainty ranges for the models, as well as enabling testing of different scenarios in the modelling. Properly integrated with all available field data, the value of seismic data is to help create a spatial understanding of geological features that cannot be achieved from well data alone.
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