This paper describes the results of a field application of borehole gravimetry to measure secondary gas saturations in a fractured limestone reservoir. Owing to its deep-reading capability and insensitivity to near-wellbore effects, the borehole gravimeter succeeded where conventional cased-hole logging methods had failed. Borehole-fluid pressure data, recorded together with the gravity data, proved useful in ensuring that the density data had the necessary high accuracy. This paper additionally presents modelling results that indicate the potential usefulness of time-lapse borehole gravity data for monitoring flood fronts remote from a borehole. This application would benefit from the development of a sensor with a very reliable absolute calibration and low drift. Introduction The potential of borehole gravimetry In hydrocarbon exploration and production was recognised already in 1950 by Smith, and the results of such measurements have been reported since 1966. Several applications of the technique have become established, and additional applications have been suggested on the basis of modelling studies (see, for example, Ref. 4). Borehole gravimetry has attractive characteristics. A borehole gravimeter (BHGM) has a large radius of investigation, and the formation bulk density derived from its gravity data is hardly influenced by the borehole fluid, casing and near-borehole features, such as mud-filtrate invasion and drilling-induced formation disturbance. Therefore, the BHGM tool is suitable for use in a cased hole. In fact, it provides the only method of obtaining the important formation bulk density measurement through casing. Its deep density measurement can sometimes be turned to good advantage in the evaluation of exploration or appraisal wells. The benefits of BHGM density measurements could be even larger if the tool were used more widely in the area of reservoir management. The tool could be applied, for example, to monitor fluid saturations averaged over a large volume or the position of flood fronts remote from a borehole. Modelling studies of these two applications have been reported, but to our knowledge no field applications have been documented. P. 151^
Typical petrophysical deliverables for volumetric and modeling purposes are net reservoir, porosity, permeability, water saturation and contact locations. These data are usually provided without quantitative determination of their uncertainties. Current computing power renders it now feasible to use Monte-Carlo simulation to determine the uncertainty in petrophysical deliverables. Unfortunately, quantitative uncertainty definition is more than just using Monte-Carlo simulation to vary the inputs in your interpretation model. The largest source of uncertainty may be the interpretation model itself. This paper will use a variety of porosity interpretation models to illustrate how the impact of each input on the uncertainty varies with the combination of input values used in any given model. It will show that use of the incorrect model through oil and gas zones may give porosity estimates with Monte-Carlo derived uncertainty ranges that exclude the actual porosity. Core data provides the best means of quantifying actual uncertainty in the petrophysical deliverables. Methodologies for deriving uncertainties quantitatively by comparison with core data will be presented. In the absence of core data, interpretation models should have been tested against core data through the same or similar formations nearby. Monte-Carlo simulation can then be used as an effective means of quantifying petrophysical uncertainty. Comparisons between the core comparison and Monte-Carlo techniques will be made, showing that similar results are achieved with the appropriate interpretation models. The methodologies described in this paper are straightforward to implement and enable petrophysical deliverables to be treated appropriately in volumetric and modeling studies. In addition, quantification of petrophysical uncertainty assists in operational decision-making by letting users know how reliable the numbers produced actually are, and what range of properties is physically realistic. Such work also allows the key contributions to uncertainty to be defined and targeted if overall volumetric uncertainty must be reduced. Introduction Petrophysical evaluations are carried out for a number of different purposes, including operational decision-making, volume in place estimation and reservoir modeling. In all cases, the uncertainty in the deliverables of net reservoir, porosity, permeability, water saturation and contact locations are critical. However, these data are usually provided without quantitative determination of their uncertainties. This paper will highlight the ease with which uncertainties can be derived using Monte-Carlo simulation. It will also illustrate how flexible this technique is when it comes to working with different interpretation models, which is not commonly done. The largest source of uncertainty in petrophysical interpretation may be the interpretation model itself. Given the large number of possible interpretation models for all the different petrophysical deliverables, this paper will only use the most basic petrophysical deliverable, being porosity, to illustrate the relationship between uncertainty and the log interpretation model selected. It will also be shown that verification of log porosity using an independent measure such as core porosity can also provide quantitative uncertainties allowing comparison with the log derived uncertainties. The State of Uncertainty in Petrophysics The requirement for quantification of petrophysical uncertainty is not a recent development. Many papers are in the literature describing functions for uncertainty definition and how to use Monte-Carlo modeling for the same purposes. Although work such as that of Amaefule & Keelan (1989), Chen & Fang (1986) and Hook (1983) provides an excellent foundation on which to calculate uncertainties, the methodologies are both time consuming to program and inflexible with regard to interpretation model. With the computing power available on desktop machines today, engineers no longer have to use these analytical techniques to derive uncertainty. Monte-Carlo models are straightforward to build and no longer time consuming to run. The literature contains a number of examples of Monte-Carlo simulation being used to characterize petrophysical uncertainty, such as the work of Spalburg (2004).
Following partial implementation of gas/oil gravity drainage as the primary recovery process in the Natih Field, Petroleum Development Oman undertook a gas saturation monitoring campaign to determine its effectiveness. Evaluation based on thermal decay tool measurements produced secondary gas saturations lower than anticipated from material balance considerations. It was reasoned that the limited depth of investigation of the nuclear tools may have been insufficient to sample formation representative of the reservoir. In order to resolve this uncertainty a number of wells were surveyed using the borehole gravimeter as a deep reading formation density tool. Comparison with original porosity measurements obtained using conventional devices enabled quantitative gas saturations to be evaluated. Confidence in the method was obtained by additionally running the tool across the same formation in a gas reservoir. Results indicate that the gravimeter can be used to quantify gas saturations to within a 15% range. The gravimeter has proven effective in situations where conventional neutron and thermal decay gas monitoring tools are limited by their depth of investigation. The accuracy of the method is constrained by the control of distance between discrete gravity stations, the borehole and cable noise and the confidence with which the reservoir porosity is known. In low porosity systems the accuracy is degraded. As a consequence of the higher gas saturations evaluated using the Borehole Gravimeter, implementation of full scale gas/oil gravity drainage in the Natih Field will continue.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIt is common practice in the petrophysical community to either calibrate log porosities to core measurements or to use the porosities, permeabilities and water saturations measured on core to verify that the log interpretation models used are appropriate. This paper advocates the latter approach, since such "tested" or "verified" models are more likely to be valid away from the cored wells and intervals. Using the core data to verify the log interpretation models also allows the log and core data to be compared and the uncertainties in the logderived properties to be quantified. This paper describes quantitative methodologies for comparing log-derived porosities, permeabilities and water saturations with their core-based equivalents. The methodology uses the differences between the log and corebased estimates to quantify uncertainty ranges at any arbitrary level (e.g. P90, P50 and P10).Quantitative comparisons are at their most useful when the uncertainty ranges derived can be placed within a known framework so that it is apparent whether or not the match is a "good" one. This paper uses experience gained from a number of Fields spread through a variety of Basins to show sort of comparisons are common. From this "Field experience" it is possible to determine whether or not log-to-core matches are within the expected range i.e. whether or not the match is "good". If it is not "good" then additional refinements may be required to improve the comparison to an acceptable level. In any case, the uncertainties associated with the log-derived estimates can be quantified.In general, the mean difference between log and core porosities should be less than 0.6 p.u., that between log and core permeabilities should be less than a factor of 1.5 and between log and capillary pressure (saturation-height) based water saturations should be less than 6 s.u.
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