No abstract

Pressure testing in very low-mobility reservoirs is challenging with conventional formation-testing methods. The primary difficulty is the over-extended build-up times required to overcome wellbore and formation storage effects. Possible wellbore overbalance or supercharge are additional complicating factors in determining reservoir pressure. This paper addresses the above technical complications and estimates petrophysical properties of low-mobility formations using a newly developed adaptive-testing approach. The adaptive-testing approach employs an automated pulse-testing method for very low-mobility reservoirs and uses short drawdowns and injections followed by short pressure stabilization periods. Measured pressure transients are used in an optimized feedback loop to automatically adjust subsequent drawdown and injection pulses to reach a stabilized pressure as quickly as possible. The automated pulse data is used to determine supercharge effects, formation pressure, and mobility via analytical models by analyzing the entire pressure sequence. A genetic algorithm estimates additional reservoir parameters, such as porosity and viscosity, and confirms results obtained with analytical models (reservoir pressure and permeability). The modeled formation pressure exhibits less than 1% difference with respect to true formation pressure, while the accuracy of other parameters depends on the number of unknown properties. As a quicker method to estimate reservoir properties, a direct neural-network regression of pulse-testing data was also investigated. Synthetic reservoir models for low-mobility formations (M < 1 μD/cp), which included the dynamics of water- and oil- based mud-filtrate invasion that produce wellbore supercharging were developed. These reservoir models simulated the pulse-testing methods, including an automated feedback-optimization algorithm that reduces the testing times in a wide range of downhole conditions. The reservoir models included both simulations of underbalanced and overbalanced drilling conditions and enabled the development of new field-testing strategies based on a priori reservoir knowledge. The synthetic modeling demonstrates the viability of the new pulse-testing method and confirms that difficult properties, such as supercharging, can be estimated more accurately when coupled with the new inversion techniques.

In today's drilling environments and economics oil companies demand focus on all aspects of services delivered by the oil field service companies. Safety and efficiency are paramount with cost considerations following closely behind. Reductions of non-productive time are continuously monitored to evaluate service delivery excellence. All economics are impacted by dead or non productive time associated with failed or damaged equipment, logistical complexities, unplanned events (i.e., weather storms), complex reservoirs and drilling trajectories.A costly yet extremely critical service for the oil companies is the capture of representative fluid samples from the reservoirs they are drilling. This is done today with formation sampling wireline tools (FSWL) lowered into the wellbore days after the drilling of the formation. The timeliness of this information as well as the additional rig costs associated with the capture of this information can be several days or more in additional time for the sampling operation. In many cases, such as in high angled or horizontal wells, the drill pipe is required to convey the wireline to the formations. In these instances the tools cannot be gravity conveyed, which adds cost and additional risk. Because formation sampling while drilling (FSWD) technology has recently been introduced, significant cost savings are possible because the testing and sampling can be performed during the drilling process. It is the objective of this paper to demonstrate an economic model using field examples to evaluate the cost benefit between using FSWL and FSWD fluid sampling operations acquired in deep water fields. Factors considered are the operating costs for FSWL and FSWD, the probability for fishing, reduction in pumping times associated with lower invasion for FSWD and reducing the operational time in high angel wells and the ability to make changes in well construction.

Physical fluid samples collected in situ provide evidence for verification of exploration prospects, optimization of formation evaluation and reservoir production. Downhole fluid analyzers (DFA) are developed essentially to ensure the quality of formation samples. Advanced DFA are emerging for more advanced fluid compositional analyses in situ, as well as for studying the effect of pressure on fluid physical and chemical properties, those typically determined in the laboratories. Laboratory tests such as PVT (pressure-volume-temperature) analysis are still used as reference in reservoir engineering, provided the sample tested is representative of formation fluid and also differences among different laboratories are minimized. This study focuses on crude oil compositional analyses during pumpout with a wireline formation tester. It summarizes experience with the in-situ measurement of methane, ethane, propane, saturates, aromatics and GOR based on multivariate optical computing (MOC) conducted at over 200 pumpout stations in a total of 37 wells drilled with a variety of inclinations, bit sizes, drilling fluids in several oil and gas fields. The results and lessons learnt enhanced technology development including hardware improvements, capability expansion for new components, processing software upgrades and the foundation of a local center of excellence for operations and study support. Examples of individual pumpout stations within the context of an integrated petrophysical analysis of wireline logs are presented to demonstrate data quality control and basic interpretation in oil and gas wells in the presence of water- and oil-based muds. The data are cross-validated by correlations with laboratory and other sensor data. Fine but consistent field-wide compositional variations suggest the possibility of new geological understanding and advanced reservoir fluid modeling from the newly acquired DFA data base.

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