Forward modelling was applied to correct formation pressures measured while drilling on a wiper run for the effects of supercharging. Supercharging is increased sand face pressure caused by drilling fluid filtrate leak-off. The study is carried on one of the well-known carbonate reservoir of the North Sea. This reservoirs, in general, exhibit good porosity; however they have poor permeability because of small pores. Pressure variations near the wellbore are primarily influenced by near-wellbore drilling fluid filtrate invasion and filter-cake formation. In general, the lower the sand-face permeability, the higher the variations. Considerable progress has been made towards understanding how filter cake forms and how it influences the near-wellbore pressure stability. Available analytical and numerical models in generally focus on dealing with "initial spurt loss" only, remaining transition and dynamic periods are assumed to be negligible. However the dynamic period, which incorporates possible erosion, plastering, clogging, and other implications, can be modelled if the sand-face (near-wellbore) is exposed to controllable and quantifiable influences. The greater number of planned quantifiable influences, the better the forward modelling. The coupled filter cake growth and formation pressure model incorporates; the geometry of the well and the drilling assembly, the time sequence of the drilling or wiper operation, and drilling fluid and formation properties. A total of 52 formation pressures were acquired during wiper run, across several thousands of horizontal section drilled in to the chalk reservoir. Pressure tests were evenly distributed to evaluate possible faults, depletion, and pressure barriers, and, more importantly, to calibrate the flow model for the future drilling campaigns. Tests acquired at same depth interval with different circulation rates were used as primary the calibration point for the forward model calibration. A secondary calibration point was obtained by from two consecutive tests, during which first circulation was kept off, and then turned on. These simulations are also applicable to exploring system behavior and responses when planning and executing the job, assessing the feasibility and suitability of the methodology to check that assumptions are satisfied, and building some expectations about the likely measured pressures and their behavior over time.

This paper was presented as part of the student paper contest associated with the European Petroleum Conference. Abstract This study aims at the generation of permeability and porosity distributions conditioned to geostatistical (variogram, mean values of permeability and porosity), static (well-logging, core etc.) and well-test pressure data and the assessment of uncertainty in generated distribution and performance prediction. Integration of all the available data from different sources(static, well test, geostatistical, etc.) into reservoir description is prerequisite for reliable performance predictions. It is shown that reservoir characterization solely based on geostatistical, static or well test pressure data imposes a nonunique problem to be solved. Specifically, by using 1-D and 2-D numerical flow simulators, we show that the pressure transient response obtained at a single well cannot provide unique determination of permeability. This necessitates the direct incorporation of well test pressure data as well as geostatistical and static data into reservoir description to resolve porosity and permeability distributions inlateral (or inter-well) directions. It is shown that the inverse problem theory based on Bayesian statistics provides a powerful methodology to incorporate different sources of data into reservoir characterization. Moreover, it provides means to assess the uncertainty in reservoir description generated and performance predictions based on such descriptions. By assuming multinormal distributions for porosity and log-permeability fields, an application of Bayesian estimation methodology to reservoir characterization is presented. Introduction It has long been recognized that reservoir rock heterogeneities have strong influence on the performance of reservoirs during waterflooding and/or gas injections and EOR techniques. Thus, a better description of reservoir rock property (such as permeability and porosity) fields is essential to make correct reservoir performance predictions. The appropriate way to reach this goal is to integrate all the available data from different sources; geological, petrophysical, geophysical, and production data. However, how to effectively integrate these data is a challenge to the person or team working in the field of reservoir characterization requiring a multistage and cross-disciplinary work.

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It is more effective to capture clean reservoir fluids as early as possible during drilling operations, and with the realization of formation-sampling-while-drilling (FSWD), this goal is fast becoming a reality. This paper summarizes one example from Saudi Aramco and shows the benefits of this evaluation procedure. Our experience in sampling oil bearing formations drilled with water-based mud, has seen that the data from these instruments, which are now able to withstand the rigors of the drilling environment, can be used in real-time to control the sampling process, capture high quality samples, and also place the well where required. In this case study, a tight carbonate reservoir was the objective with the oil column being near saturation pressure. Under these circumstances, the challenges presented to us while sampling, included maintaining low flow rates and potential three-phase conditions, due to unavoidable drawdown while minimizing the time on the wall. During sampling, pump efficiency was consistent with low-mobility formations, even with the various multiphase flow regimes encountered. Hydrocarbon breakthrough was significantly faster than historical wireline sampling performed in the same reservoir. FSWD greatly improves our costs by saving rig time. Also, FSWD was utilized to evaluate low-resistivity-pay zones and observe if they hold a movable water fraction. We see this as crucial for geosteering wells to prevent drilling through the zones with movable water, which will enhance the productivity index of the wells. As a first time experience of FSWD in tight zones, a valuable lesson came to light where additional real-time monitoring improvements can be made, including the need to improve real time data quality, to accurately determine clean-up fractions while sampling. Based on this experience, we have formulated operational guidelines for improving real-time data analysis and determining the most opportune time to sample during the drilling process.