This paper presents a methodology which allows performing a real time characterization of the conductive natural fractures permeability intercepted by the bit while drilling. Such fractures are detected by monitoring continuously flowing from the wellbore into surrounding formations and the mud losses at the rig-site using flow-meters measuring both the ingoing and the outgoing mud flow.
Moreover, when drilling naturally fractured reservoirs, mud loss data provide one of the most effective means to assess the existence of conductive fractures intercepting the wellbore and therefore to identify potentially producing intervals.
The patterns in the variations of these volumes are analyzed to identify open fractures. The advanced Flowmeter has increased the resolution of the mud flow measurements. It has enabled the authors to assess the flow quantitatively and relate mud flow anomalies with the presence of open fractures down hole in the trial exploratory well.
The mud flow anomalies were validated with surface drilling parameters and gas indications. It was observed that the open fractures were associated with increase in torque and gas indication. The mud flow anomalies also provide crucial information for early kick or losses detection in high pressure gas wells because a better accuracy and a quicker response in detecting kicks and losses can be achieved by monitoring the changes of the mud flow rate by using flow meters measuring the inflow and the outflow mud rate, respectively.
Method and Theory The most commonly used techniques to detect the mud losses consist in monitoring the level of the mud pits with acoustic, floating sensors and/or using paddles set inside the flow line that measure the return mud flow rate with a small degree of accuracy. The traditional Flowmeter provides a simple qualitative fluctuation in mud flow. In contrast this advanced Flowmeter works on the principle of converting mudflow out in to an analog signal which represents the volume of mud.
Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics.
Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences.
In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed "Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'.
The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model.
The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.
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