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Belyaeva, Olga (Salym Petroleum Development N.V.) | Podberezhnyy, Maxim (Salym Petroleum Development N.V.) | Zverev, Vladimir (Salym Petroleum Development N.V.) | Sviridov, Mikhail (Baker Hughes) | Mosin, Anton (Baker Hughes) | Antonov, Yuriy (Baker Hughes)
Abstract West Salym is a Salym Petroleum Development oil field located in Khanty-Mansi Autonomous Okrug, 120 kilometres south-west of Surgut. The West Salym oil field was discovered in 1987 and was brought on stream in 2004. The reservoirs vary from fluvial/deltaic to shallow marine deposits. The primary development of the central area of the West Salym field is completed. The edge of the field remains undeveloped and potentially attractive. The edges of the field are presented by a mouth bar and characterized by significant structural formation changes, including unknown formation dips and the presence of local carbonate concretions and stripes. The sand thickness of the target layer is 15 m with a minimum oil height of 1m, caused by structural dip and OWC closeness. In these conditions it is difficult to drain the area with geometrically placed wells within the hydrocarbon-saturated layer based on well correlation and 3D seismic interpretation results. Another challenge is the low resistivity contrast between shale, oil- and water-bearing layers, which complicates reservoir properties evaluation and distinguishability of different fluid saturations. To evaluate capabilities of modern reservoir navigation technology, two horizontal wells (500 m each) were drilled for the first time in the West Salym field, using deep-azimuthal resistivity technology and advanced data interpretation software. Drilling both horizontal wells was improved by pilot holes (well A and D) with standard GR-Resistivity-Density-Neutron logging suite and pressure testing‥ Logging-while-drilling, deep-azimuthal resistivity technology has been used in the field development, contributing to proactive reservoir navigation. This technology provides input for the interpretation of an extensive set of multi-component, multi-spacing and multi-frequency measurements. This data are usually sufficient to resolve the formation properties in the vicinity of several meters from the wellbore and to adjust the direction of the well trajectory. However, due to time restrictions, very simple resistivity models and only the subset of data are often used for real-time interpretation. Moreover, the structure of the data subset is often predefined to provide the maximum depth of investigation, neglecting the quality of formation parameter resolution. In some particular fields it can lead to increased uncertainties during reservoir navigation. The data interpretation software mentioned in this paper has an excellent performance and enables the real-time processing of the full set of downhole measurements based on multi-layered formation models. This case highlights the first use of this software application in the Russian Federation. The software is based on the method of the most-probable parameter combination and keeps the optimal balance between the information recovered from the measured data and all available a priori knowledge about the structure. The ability to accurately involve a priori information enhances software capabilities of resolving layers with low-resistivity contrasts. Moreover, inversion software is user-controlled to carefully monitor lateral and vertical changes in the geology. The advantages of data inversion software ensured successful reservoir navigation in the challenging conditions of the West Salym field. All steering decisions were made according to the consistent and reliable multi-layered formation resistivity model that was constructed in real time during the drilling. A good net-to-gross ratio was achieved: almost 75% for one well and 50% for the other. Expected oil rates are 300 and 150 m/day. Post-drilling analysis showed that in the case of geometrical drilling without the application of reservoir navigation technology the net-to-gross ratio would not exceed 40%.
We present a study on determining electrical anisotropy in a 1D environment and its application to the characterization of anisotropic hydrogeologic parameters. The solution we have developed utilizes simultaneously the information in 1D DC resistivity and time-domain electromagnetics (TEM) data sets. Jointly interpreting 1D DC and TEM inversions enables us to determine the three parameters needed to describe the electrical anisotropic model: the transverse resistivity, longitudinal resistivity, and the layer thickness. We then demonstrate that these parameters of an anisotropic resistivity model may be used to determine hydrogeological parameters and its anisotropy. We demonstrate the validity of these connections using a synthetic example and a field data set.
Resistivity measurements have been used in the oil and gas exploration and production industry for many decades to provide valuable information relating to the determination of hydrocarbon saturation, which is a key petrophysical attribute required for accurately quantifying reserves and designing an appropriate field development strategy.
With the continuing integration of Logging While Drilling (LWD) and Directional Drilling processes in the past 20 years, many reservoirs around the world are being drilled and evaluated with LWD tools. Recent technology advancements in LWD Electromagnetic Wave Propagation Resistivity devices coupled with significant software enhancements provided dramatic improvements in well-placement applications in highly deviated and horizontal wells. However, LWD propagation resistivity measurements in these wells often present challenges for the petrophysicist in answering fundamental questions in relation to formation evaluation.
Typically, it is not only problematic to correlate LWD propagation resistivities to offset vertical and/or pilot resistivity data, but also difficult to deduce true formation resistivity (Rt) from the numerous multi-frequency and multi-spacing measurements available. The logs may also be affected to varying degrees by the borehole, eccentricity, shoulder beds, invasion, fractures, and anisotropy and/or dielectric effects. These effects may occur individually or in combination. Identifying these effects and correcting for them remains to be a major challenge.
This paper presents a case study where a new generation of LWD azimuthal deep resistivity tool has been utilized to drill a high angle well through a major carbonate reservoir sequence in onshore Abu Dhabi characterized as multilayered formation comprising of porous reservoir units separated by thin stylolite sub-dense.
Anisotropy inversion is discussed in identifying horizontal and vertical resistivities (Rh and Rv). Inverse forward modeling is also performed iteratively to evaluate the sensitivity of actual log responses to environmental and adjacent bed effects. The resultant resistivities of the porous units were found to be more representative than the measured values which were unusually higher being affected by the stylolitic sub-dense shoulder beds. These inverted resistivity values were then used for fluid volumetric calculations and compared against existing offset field data and production history. The results show reasonable water saturation values consistent with nearby wells. Inversion has therefore enabled considerable improvement in formation evaluation results by eliminating the shoulder bed effects on measured resistivity and providing accurate true resistivity (Rt).
Jian, Wang (Institute of Acoustics, Chinese Academy of Sciences) | Lei, Zhang (Institute of Acoustics, Chinese Academy of Sciences) | Hao, Chen (Institute of Acoustics, Chinese Academy of Sciences) | Xiu-ming, Wang (Institute of Acoustics, Chinese Academy of Sciences)
Real-time geosteering technology plays a key role in horizontal well development, which keeps the wellbore trajectories within target zones to maximize reservoir contact. Deep-directional-resistivity logging while drilling (LWD) tools have longer detection range and directionality to provide sufficient information for the operators, but meanwhile bring challenges to inversion of logging data, especially when the number of model layers is not fixed in priori. In this paper, we have developed an automatic inversion method to include the number of layers as a variable based on the trans-dimensional Markov chain Monte Carlo (MCMC) algorithm. The method assumes a 1D model based on planar layered formations penetrated by arbitrary well trajectories. In addition, a synthetic example demonstrates the inversion method can efficiently estimate the number of layers, positions, resistivities and also provide the probabilities of parameters without introducing bias.
Presentation Date: Wednesday, October 17, 2018
Start Time: 1:50:00 PM
Location: Poster Station 10
Presentation Type: Poster