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The most important data for designing a fracture treatment are the in-situ stress profile, formation permeability, fluid-loss characteristics, total fluid volume pumped, propping agent type and amount, pad volume, fracture-fluid viscosity, injection rate, and formation modulus. It is very important to quantify the in-situ stress profile and the permeability profile of the zone to be stimulated, plus the layers of rock above and below the target zone that will influence fracture height growth. There is a structured method that should be followed to design, optimize, execute, evaluate, and reoptimize the fracture treatments in any reservoir. The first step is always the construction of a complete and accurate data set. Table 1 lists the sources for the data required to run fracture propagation and reservoir models.
Induction logging was originally developed to measure formation resistivities in boreholes containing oil-based muds and in air-drilled boreholes because electrode devices could not work in these nonconductive boreholes. However, because the tools were easy to run and required much less in the way of chart corrections than laterals or normals, induction tools were used in a wide range of borehole salinity soon after their introduction. Commercial induction tools consist of multiple coil arrays designed to optimize vertical resolution and depth of investigation. However, to illustrate induction-tool fundamentals, it is instructive to first examine the basic building block of multiple-coil arrays, the two-coil sonde. Figure 1 shows that a two-coil sonde consists of a transmitter and receiver mounted coaxially on a mandrel. Typical coil separations range from 1 to 10 ft apart. In practice, each coil can consist of from several to 100 or more turns, with the exact number of turns determined by engineering considerations. The operating frequency of commercial induction tools is in the tens to hundreds of kilohertz range, with 20 kHz being the most commonly used frequency before 1990. Figure 1 – Schematic representation of a two-coil induction array showing the distribution of the currents induced in the formation by the transmitter coil. The induction transmitter coil is driven by an alternating current that creates a primary magnetic field around the transmitter coil.
Resistivity logging is an important branch of well logging. Essentially, it is the recording, in uncased (or, recently, even cased) sections of a borehole, of the resistivities (or their reciprocals, the conductivities) of the subsurface formations, generally along with the spontaneous potentials (SPs) generated in the borehole. This recording is of immediate value for geological correlation of the strata and detection and quantitative evaluation of possibly productive horizons. The information derived from the logs may be supplemented by cores (whole core or sidewall samples of the formations taken from the wall of the hole). Several types of resistivity measuring systems are used that have been designed to obtain the greatest possible information under diverse conditions (see links below).
Resistivity logging is an important branch of well logging. Essentially, it is the recording, in uncased (or, recently, even cased) sections of a borehole, of the resistivities (or their reciprocals, the conductivities) of the subsurface formations, generally along with the spontaneous potentials (SPs) generated in the borehole. This recording is of immediate value for geological correlation of the strata and detection and quantitative evaluation of possibly productive horizons. The information derived from the logs may be supplemented by cores (whole core or sidewall samples of the formations taken from the wall of the hole). As will be explained later, several types of resistivity measuring systems are used that have been designed to obtain the greatest possible information under diverse conditions (e.g., induction devices, laterolog, microresistivity devices, and borehole-imaging devices).
Quality control (QC) procedures are especially important during the creation of integrated computed products and to ensure optimal nuclear magnetic resonance (NMR) data acquisition. NMR tools have calibration standards and real-time QC indicators. Processed results should agree with other data from logs, core, and/or formation test results. NMR QC includes a series of prejob and post-job checks and calibrations. Several prejob QC steps are necessary to ensure reliable results.
Shales are one of the more important common constituents of rocks in log analysis. Aside from their effects on porosity and permeability, this importance stems from their electrical properties, which have a great influence on the determination of fluid saturations. Shales are loose, plastic, fine-grained mixtures of clay-sized particles or colloidal-sized particles and often contain a high proportion of clay minerals. Most clay minerals are structured in sheets of alumina-octahedron and silica-tetrahedron lattices. There is usually an excess of negative electrical charges within the clay sheets.
Commercial resistivity measurements made while drilling first became available in the late 1970s. Because the drilling environment is much more adverse than the wireline logging environment, a simple short normal tool mounted behind the drill bit was used as the first LWD resistivity tool. However, short normal tools were only able to provide information for basic interpretation, such as correlation of geological markers and estimation of gross water saturation, because of their shallow depth of investigation and relatively poor vertical resolution. Being DC electrode devices, normal tools are also limited to conductive mud environments. To expand the LWD resistivity market to oil-based mud (OBM) environments, induction-type propagation measurements were introduced in the early 1980s.
Clegg, Nigel (Halliburton) | Duriez, Alban (Halliburton) | Kiselev, Vladimir (Halliburton) | Sinha, Supriya (Halliburton) | Parker, Tim (Halliburton) | Jakobsen, Fredrik (Aker BP) | Jakobsen, Erik (Aker BP) | Marchant, David (Computational Geosciences Inc.) | Schwarzbach, Christoph (Computational Geosciences Inc.)
Abstract Mature fields contain wells drilled over decades, resulting in a complex distribution of cased hole from active producers, injectors, and abandoned wells. Continued field development requires access to bypassed pay and the drilling of new wells that must be threaded between the existing subterranean infrastructure. It is therefore important to know the position of any offset wells relative to a well being drilled so collision can be avoided. A well’s position is determined by directional survey points, for which the measurement error accumulates along the length of the well, increasing the uncertainty associated with the well position. The positional uncertainty is greater in wells drilled with older generations of surveying tools. Thus, a new well may be required to enter the ellipse of uncertainty representing the potential position of an older well, risking collision, to be able to reach desired targets in more distal parts of the reservoir. A potential solution to reduce collision risks is ultra-deep electromagnetic (EM) logging-while-drilling (LWD) tools, whose measurements are strongly influenced by proximity to metal casing and liners. This paper presents 3D inversion results of ultra-deep EM data from a development well in a mature field, which were used to identify a nearby cased well. Due to the large effect of casing on the measured EM field, it is important to validate the 3D results; this has been achieved using a synthetic modelling approach and assessment of azimuthal EM measurements. Models were created with casing positioned within resistive media with similar properties to those seen in the studied cases. Inverting these models allows testing of the inversion algorithm to show that it is providing a good representation of the cased well’s position relative to the newly drilled well. Further analysis of recorded and synthetic data showed that the raw EM field is strongly influenced as the casing is approached. The casing can be seen to clearly affect the EM field measurements when it is in the region of 10 to 15 m ahead of the EM transmitter, with the effect increasing in magnitude as this distance diminishes. Modelling shows that the EM field measurements behave in a predictable manner. As the ultra-deep EM tool approaches a cased well, it is possible to determine whether the casing is above, below, or critically, directly in line with the planned trajectory of the new well. Existing subterranean infrastructure can pose a major hazard to the drilling of new wells. Being able to identify an old well ahead of the bit using ultra-deep EM measurements would allow a new well to be steered away from the hazard or drilling stopped, preventing a collision. In addition, this may also allow the drilling of well paths that would otherwise be impossible to drill, due to the limitations imposed by positional uncertainty of the new and offset wells. This use of ultra-deep resistivity technology takes it beyond its more traditional benefits in well placement and formation evaluation, making it useful for improving well drilling safety.
Abstract Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. Enhanced resistivity models for shaly-sand analysis include clay concentration and clay-bound water as contributors to electrical conductivity. These shaly-sand models, however, consider the existing clay in the rock as dispersed, laminated, or structural, which does not reliably describe the distribution of clay network in organic-rich mudrocks. They also do not incorporate other conductive minerals and organic matter, which can significantly impact the resistivity measurements and lead to uncertainty in water saturation assessment. We recently introduced a method that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to verify the reliability of the introduced method for the assessment of water/hydrocarbon saturation in the Wolfcamp formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and non-conductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, the conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we develop two inversion algorithms (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. Rock type, pore structure, and spatial distribution of rock components affect geometric model parameters. Therefore, dividing the formation into reliable petrophysical zones is an essential step in this method. The geometric model parameters are determined for each rock type by minimizing the difference between the measured resistivity and the resistivity, estimated from Pore Combination Modeling. We applied the new rock physics model to two wells drilled in the Permian Basin. The depth interval of interest was located in the Wolfcamp formation. The rock-class-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 32.1% and 36.2% compared to Waxman-Smits and Archie's models, respectively, in the Wolfcamp formation. The most considerable improvement was observed in the Middle and Lower Wolfcamp formation, where the average clay concentration was relatively higher than the other zones. Results demonstrated that the proposed method was shown to improve the estimates of hydrocarbon reserves in the Permian Basin by 33%. The hydrocarbon reserves were underestimated by an average of 70000 bbl/acre when water saturation was quantified using Archie's model in the Permian Basin. It should be highlighted that the new method did not require any calibration effort to obtain model parameters for estimating water saturation. This method minimizes the need for extensive calibration efforts for the assessment of hydrocarbon/water saturation in organic-rich mudrocks. By minimizing the need for extensive calibration work, we can reduce the number of core samples acquired. This is the unique contribution of this rock-physics-based workflow.
Abstract Electromagnetic propagation logging has been primarily used to measure formation resistivity. A sensitivity study shows that the formation dielectric constant becomes detectable when it is larger than 10 for the typical LWD 2 MHz propagation measurements. At this frequency, in some field cases, the dielectric constant can be tens to hundreds, making it measurable directly from propagation data. Factors causing this high dielectric constant include connate water volume, the interfacial polarization due to clays, and the Maxwell-Wagner effect as a result of coexistence of conducting and insulating materials. Current methods for determining dielectric constant are based on a homogeneous assumption for the formation regardless of its actual complexity. These methods give reasonable results in thick beds (larger than 10 ft) and low-resistivity-contrast (less than 5) formations. In thinner beds with larger resistivity contrast, both resistivity and dielectric constant logs can be adversely affected by the strong shoulder bed effect. The dip effect in dipping formations can only exacerbate the situation. Like resistivity, dielectric constant can also be anisotropic, but the anisotropy will be ignored here. These effects must be corrected to mitigate undesirable results in the quantitative use of resistivity and dielectric constant logs. The goal of this paper is to address this problem by incorporating the layered structure in the formation model to correct for the shoulder bed and dip effects on resistivity and dielectric constant logs. To account for these effects, we first take advantage of our previous work (Wang et al., 2019) on induction dielectric processing for its fast convergence, robustness to data noise, and weak dependence on initial formation model. The regularization popular for feature selection problems in supervised learning is then added to the existing method. An attractive feature of this regularization is its unique noise-suppression capability while being able to preserve bed boundaries on resistivity and dielectric constant logs. Numerical experiments with synthetic data demonstrate the clear benefits of the new processing in comparison to current methods that have been popular for dielectric constant processing. Field testing also confirms that this data processing is superior to the current methods as far as dip and shoulder bed effects are concerned. Both synthetic and field results indicate that this advanced data processing should be run preferentially as long as the relative dip is not extremely high (less than 70 deg). Some questions with practical importance are addressed in detail that provides insight into the characteristics and performance of the processing. These questions include the effects of data noise, inaccurate dip input, and drilling fluid invasion. In addition, the depth of investigation and vertical resolution are studied for resistivity and dielectric constant to enable a quantitative comparison with other logs. The limitations of the processing and guidelines are also discussed before field applications.