Combined Approach Improves Fault Description for Horizontal-Well Geosteering
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This paper presents an interdisciplinary approach to the description of tectonic dislocations made on the basis of interpretation of seismic data, petrophysical analysis of well-logging data in horizontal wells, and inversion of a multifrequency propagation tool. A consistent approach to fault identification and description is presented on the basis of seismic surveys and logging-while-drilling (LWD) data in horizontal wells in a western Siberian oil field.
Seismic Methods of Tectonic-Fault Interpretation
Estimation of seismic methods of fault detection was performed on materials acquired from one of the fields in the Frolov oil-and-gas district. The observed territory of the oil field is characterized by complex geological structure—namely low effective reservoir thickness, thin layering of sandstones and silts, low porosity, low-permeability reservoir zones, and tectonic block structure.
When drilling in reservoirs of low thickness, knowing the precise position of the horizontal wellbore relative to the structure is critical. The basis of drilling planning is a structural map of the reservoir top. After field jobs were complete and the results of the wide-aperture 3D seismic survey of the considerable refinement of the top structure of the reservoir were obtained, the morphology of all earlier identified structural forms was completed. A faulted and blocked principal model of the reservoir was created that formed the basis for a well-pad positioning scheme, as well as for usage in horizontal-well drilling.
Horizontal Well 2301G was planned in the northwest region of the field. For reservoir-model construction, a structural map prepared from seismic-survey interpretation was used. An outcrop of the structural map with Well 2301G in place is presented in Fig. 1.
A rare well network in the drilling area led to high uncertainty regarding structure parameters and risk of penetrating a fault. As an offset, Well 75B3 was used (Fig. 1). In this offset well, the horizon AC3 represented a thin layering of silt, shaly sandstone, and tight rocks. The thickness of the AC3 horizon was 13.2 m. During the drilling of the transport section, the wellbore of Well 2301G penetrated the top of the AC3 horizon at 1912.12-m true vertical depth, which coincided with the structural surface prognosis made on the basis of seismic data.
The blocked model of the AC3 horizon is complex; the vision of the structure of the target reservoir can change significantly while drilling and penetrating the reservoir. According to seismic data, the possibility exists of crossing three faults along the planned horizontal section of Well 2301G.
After penetration of the top of the target horizon, the well profile was stabilized at 90° within AC3. At a measured depth (MD) of 2883.5 m, an abrupt increase of gamma measurements occurred that was interpreted as fault crossing and coming out to silt. In this oil field, the top shales have a different composition from the bottom shales, according to mud analysis. The top shales above the AC3 horizon consist mostly of argillites (up to 85%), whereas the bottom sediments consist mostly of sandstones and silts, with a gradual increase in argillite.
According to the structural map, either an increase of the top structure or a fault of the thrust type in the interval 3585.0 m ±50.0 m was expected (also interpreted from seismic data). A recommendation to continue drilling the horizontal section with stabilization at 90° was proposed. The drilling was continued in argillites above the horizon.
At an MD of 3658.0–3676.0 m, the wellbore of Well 2301G crossed the second tight layer. According to the geonavigation model, the drilling was performed in the target reservoir below the tight layer. The drilling of the horizontal section was completed at a final depth of 4289.0 m. Lithological and stratigraphic composition of the AC3 horizon was confirmed.
The information about the horizon’s top-structure behavior received while analyzing seismic data allowed the ability to maintain a slightly sloping trajectory of the well and to establish optimal drilling in a complex structural and tectonic environment. Thus, actual analysis of available seismic data allowed accurate decision-making about horizon position relative to the encountered faults.
Statistical and Inversion Methods for Fault Description
Multivariate Statistics in Lithofacial Analysis. The approach to the lithological description resulting from interpretation of well-log data in horizontal wells is different from that used with slightly deviated and vertical wells. Aside from the differences associated with the drilling environment and its effect on well-logging measurements, the main difference in well-log interpretation in horizontal sections is the approach to how lithotyping and the stratigraphic breakdown along the horizontal borehole are performed.
In horizontal wells, petrophysical analysis of the crossed formations has to be extended and complemented with methods of geonavigation that solve the problem of positioning of the wellbore; this process should be based on qualitative and quantitative identification of penetrated lithotypes from a variety of well-logging methods and structural construction during the drilling.
In vertical wells, lithological breakdown is performed under the assumption that layers that form some order in the vertical direction maintain this order along the MD in the borehole. However, this rule of stratigraphic ordering may not be true in deviated and horizontal wells. The main reason for crossed lithotypes not following each other along the horizontal borehole is the highly ambiguous and unpredictable lateral changes of rock types that may occur. Description of lateral rock changes is a complicated task because of the following:
- Mechanisms of sedimentation
- ost-depositional processes (wreathing and erosion)
- Relative shifts and transport of rock masses caused by tectonics
Correct lithotyping allows, besides justified application of petrophysical models for porosity/permeability estimation, the application of results of lithotyping for horizontal-wellbore positioning after fault crossing, with further optimization of the geonavigation model.
The part of the AC3 formation associated with Pad 16 is characterized by heavy compartmentalization because of active tectonics. Only some of the faults were predicted by seismic-data interpretation; a considerable number of faults were detected only during drilling on the basis of direct and indirect features. At the stage of well planning and decision-making while geosteering in the faulted environment, the ability to describe a fault in detail on the basis of a combination of a priori knowledge and results of areal and borehole geophysical surveys has significant importance. Because of low spatial resolution and, often, the low quality of seismic data, it is often only possible to flag the existence and absence of the fault during seismic cross-sectioning along the horizontal wellbore. On the other hand, well-log data have higher resolution along the borehole and a higher density of measurements and can describe the penetrated rock quantitatively. A lithofacies-analysis method applied on well-log data allows greater precision in describing the crossed rock in order to justify the position of the wellbore.
LWD Resistivity-Data Inversion Near the Faults. As a case study of detailed analysis of fault parameters combining the lithofacies breakdown and electromagnetic inversion of the induction measurements in the AC3 formation, Well 5842G was analyzed. The well crosses an assumed fault at an MD of 3635.0 m. However, on the basis of detailed density-image interpretation, another fault at 3485.0 m was assumed. Thus, on the basis of direct fault detection from density images and indirect signs of lithotyping order from lithofacies analysis, the final updated geological model with two faults was proposed.
The wells in the pad were placed using LWD deep-resistivity measurements. These measurements make estimation of layer resistivity possible. Inversion in the framework of 1D layered and 2D formation models is proposed to improve the formation geoelectrical model near the fault zones.
Deep-resistivity tools are sensitive up to 3–5 m from the well. Because of the axial symmetry of the transmitter and receiver coils around the tool axis, measurements are devoid of azimuthal sensitivity. Therefore, a priori information about lithological structure and layer resistivities is necessary to reduce geological uncertainty. An expected geoelectrical-formation model is based on the preliminary petrophysical analysis of the data and the refined geonavigation reservoir model. The geoelectrical-formation model improves in two stages: multiparametric inversion in the 1D layered formation model and multiparametric inversion in the 2D formation model.
The scheme of data interpretation within the framework of the 1D layered model should be described briefly. An expected 1D layered reservoir model with a specified number of layers is set up along an MD of a well at a certain interval. The positions of the boundaries between the layers relative to the tool, the resistivities of the layers, and the dip angle of the formation are adjusted with ranges for all the parameters. For each parameter, restrictions are in accordance with a priori information. In the study case, in the absence of azimuthal sensitivity of the measurements, constraints on the parameters are established from the refined geonavigation model. The inversion algorithm minimizes the objective function consisting of the sum of the residual between the synthetic and measured curves, the deviations between the recovered and expected parameters of the model, and the penalty functions responsible for parameter constraints. The residual is calculated for the attenuation amplitudes and phase differences and is normalized to the standard tool error.
Simulation and inversion in the 2D formation model are performed using an algorithm based on the method of boundary integral equations. The 2D model is constructed on a certain interval starting from the set of 1D layered models at smaller intervals. According to the refined geonavigation model of the reservoir, the faults are at depths of approximately 3485 and 3635 m MD.
On the basis of the 1D layered model, an expected 2D formation model is constructed. According to the seismic data, the faults are subvertical. In this case, the tool measurements are weakly sensitive to the fault dip angle. Therefore, in the expected 2D formation model, the fault is considered vertical and the position of the fault plane along the MD within the interval of 3480–3492 m is recovered.
As a result of 2D modeling and inversion, the 2D formation model near the fault is recovered and the position of the fault plane is adjusted at the MD of 3488 m.
Fig. 2 shows the measured and synthetic logging curves of the 1D and 2D models. The synthetic curves obtained in the 2D model are smoother and do not have sharp jumps. In the interval of 3484.5–3485.5 m, in accordance with the measured data and the density image, the well trajectory intersects the subvertical high-resistance inclusion, which is considered and fitted in the 2D model. The fault throw amplitude is approximately 3.9 m.
The resistivity-log curves are interpreted near the second fault at the interval of 3630–3640 m. From the variation of the curves along the depth, it can be concluded that the contrast of the resistivity of the layers in which the tool is located before and after the fault is significant.
A combined approach to fault identification and description on the basis of 3D seismic surveys and detailed analysis of LWD well-logging data was observed. The proposed approach includes step-by-step retrieval of information about faults existing in the target geological zone with subsequent specification and adjustment of the fault parameters. The areal 3D seismic surveys are the main method used for fault identification, and their continuity provides the greatest volume of information about the structure of the faulted reservoir. However, because of limitations in the spatial resolution of seismic data, these data have the lowest accuracy in fault parameters.
Qualitative and quantitative interpretation of LWD well-logging data provide much higher vertical and lateral resolution of the formation inhomogeneity because of higher resolution along the wellbore and higher measurement density. In the case of well logging in deviated and horizontal wells, the interpretation of well-log data is complicated by lateral variability of certain lithotypes. Machine-learning methods for analysis of multivariate data from LWD well-logging tools, in particular nonlinear dimensionality-reduction algorithms, allow efficient visualization and clustering of the measured data and completion of the detailed stratigraphic breakdown. Lithofacies analysis, along with traditional image analysis, allowed improvement of the geonavigation model.
The most-accurate and, at the same time, most-demanding approach for computational resources and the quality of the initial geological model is 2D electromagnetic inversion of the resistivity data near the fault surface. The inversion allows modeling of realistic signals of resistivity tools and detailed estimation of parameters such as the dip and angle of the studied fault.
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Combined Approach Improves Fault Description for Horizontal-Well Geosteering
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