This paper will describe a new method for improving Logging While Drilling (LWD) depth accuracy. Case studies that describe this technique will also be presented. It is generally accepted that using the Drillers depth measurement for LWD applications has been the most practical solution to a complex depth problem. The various sources of depth errors have also been described and quantified in the industry. Two of the main contributors to drillpipe based depth error are mechanical stretch and thermal expansion. Of these, the mechanical stretch is governed largely by the well profile, linear weight of the pipe, and the frictional forces that can be calculated using industry standard torque and drag calculations. The coefficient of linear thermal expansion of the drillstring components and the distributed temperature of the drillstring assembly at the time of measurement govern the change in length due to temperature. To compensate for these effects requires a series of algorithms that identify the mechanical condition of the drillstring at the time of measurement based on the operational drilling mode. Then, a standard torque and drag model is used to calculate the mechanical stretch, and a thermal expansion algorithm subsequently applies the temperature component of the depth correction. The results of these computations are a corrected logging depth, and an improved time to depth conversion file that can be used to recalculate the logging data. The results from case study data strongly support that; uncorrected standard LWD depth accuracy today is often at least as good as that provided by comparable wireline logs; and that LWD depth can be significantly improved using this technique. This new method for improving logging depth will lead to enhanced single well evaluation and the improved well-to-well correlation of reservoir features, and hence the value of the reservoir model. Drillers Depth LWD measurements are referenced to Drillers depth, and this is generally based on a listing of hand measurements made on each length of pipe lowered into the well. To provide a continuous depth for the log data, the movement of the block is tracked at surface, using a geolograph or drawworks encoder1 (figure 1). For each foot that the block travels up or down, it is assumed that the bit travels and equal distance out of or into the hole. Bit movement is only updated when the pipe is apparently "out of slips". In and out of slips is determined by software that monitors a hook-load sensor attached to the drilling deadline. As drillpipe is moved up and down in the well, the LWD depth tracking inevitably starts to deviate from Drillers depth due to errors in determining in and out of slips, depth sensor calibration errors and errors in the pipe tally itself. As a result, in practice the LWD depth is periodically adjusted to match driller's depth. Measuring the bit depth (and hence the LWD true sensor depth) at surface also neglects the changes in pipe length due to hole geometry, temperature and mechanical stretch. Surface depth measurements assume that the drill pipe is a rigid body and that any movement at surface is immediately translated into the equal movement of the bit down-hole, which may be several miles away. So even if it were possible to make a perfect depth measurement at surface, bit depth would still be in error.
LRLC reservoirs are increasingly at the forefront of the industry's concern in diverse projects ranging from offshore deep-water exploration of turbidites to the development of brown-field secondary objectives. Although LRLC reservoirs have been under production for many years, their identification and the calculation of their reserves and flow properties remains a difficult challenge. This paper compares different petrophysical workflows for clastic reservoirs where thin conductive laminations and high bound water fraction are the source of low resistivity and contrast, with a view to reducing uncertainty in saturations and improving producibility prediction. When thinly laminated reservoir layers are intercalated with conductive non-reservoir layers, the apparent formation resistivity is dramatically reduced and the apparent clay volume is increased, and the hydrocarbon volume and the permeability calculated from conventional petrophysics are underestimated. We describe new developments in laminated sand analysis and the practical implementation of resistivity anisotropy, including corrections for clay intrinsic anisotropy and thin non-reservoir resistive layers. Reservoirs with fine grain material, grain-coating clays, or dispersed clays may display high bound water volumes, yet possess significant quantities of producible hydrocarbon. While conventional petrophysical analysis can provide reliable water saturation, it does not distinguish clay- and capillary- bound water from free water. Also, shaly and silty reservoirs often present a complex mineralogy which makes estimates of clay volume and grain density uncertain. We describe the application of nuclear spectroscopy and NMR logs to calculate clay volume, porosity and bound water volume and illustrate their impact on the quality of the resulting evaluation. Although the petrophysical methods presented were developed for thinly bedded reservoirs, we show that they can improve the analysis of both LRLC and conventional clastic reservoirs. In particular, we propose fit-for-purpose workflows that reduce the uncertainty of fluid volumes and rock flow properties.
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