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Abstract The Sea Lion Field is an Early Cretaceous turbidite fan complex, located in the North Falkland Basin, 220 km north of the Falkland Islands. The reservoirs are dominated by amalgamated high density turbidites (Bouma Ta and liquefied sediment gravity flows), but also contain low density turbidites, linked debrites and interdigitated lacustrine mudstones. An integrated dynamic modelling workflow which incorporates the latest understanding of the Sea Lion Field sedimentology and reservoir heterogeneities is presented. The workflow focuses on capturing and retaining reservoir heterogeneity throughout the reservoir modelling process. Coarse-scale heterogeneity is captured during the construction of the full-field geological (static) model and conserved in the dynamic model by using the same grid dimensions. Sedimentological features (fine-scale heterogeneity) below the grid resolution are captured in separate, 3D core-scale models. Through a process of kv/kh and relative permeability upscaling, the core-scale models are used to inform effective permeability in the full-field model. Detailed interpretation of the available core data enables a statistical evaluation, which underpins the construction of core-scale models for the individual rock types. The resulting 3D core-scale models are representative of the reservoir and the development concept in terms of reservoir dip, lithology, petrophysical and fluid properties and well spacing. Matching the coarse model behaviour to the core-scale model forecast is an inverse problem with multiple possible solutions; therefore, assisted history matching is a valuable tool for quickly obtaining, comparing and ranking possible upscaled relative permeability functions and kv/kh ratios. The upscaled relative permeability functions output from the assisted history matching workflow correct for numerical dispersion and reproduce the waterflood behaviour observed in the core-scale model, thus capturing the influence of small-scale heterogeneities. This integrated dynamic modelling workflow allows for the direct use of detailed geological models characterising the main heterogeneities impacting flow behaviour, while retaining the ability to investigate and capture small-scale heterogeneities below the resolution of the full-field static model, thus avoiding the cumbersome process of upscaling geological properties. Assisted history matching and optimization have been integrated into the workflow, providing a robust method to produce upscaled relative permeability functions that replicate the expected waterflood behaviour.
This paper proposes a new reservoir characterization scheme that integrates geological facies from borehole- resistivity image logs, mud logs, and nuclear-magnetic-resonance (NMR) logs for reservoir quality assessment. Combining geological facies with NMR measurements enables the optimization of formation pressure testing and the detection of high-quality rocks for fluid flow.
Borehole image and mud logs were used to classify four geological facies based on the sequence by Bouma et al. (1962). The sequence in each sand body was detected and correlated with neutron and density logs. Then, the NMR log data were used to calculate the ratio of free fluid index (FFI) to bulk volume irreducible (BVI). A large FFI-to-BVI ratio indicates better rock quality. Combining rock quality from the Bouma sequence and FFI-to-BVI ratio eliminates the poor-quality rocks in pressure-testing point selection to establish a valid fluid gradient.
The borehole image and mud logs in turbiditic beds indicate the energy level of the current transporting the sediments. Four lithofacies were identified in the turbidite deposit environment based on these well logs. Facies A has the highest energy and tends to roll across underlying beds, leaving gouge marks and entraining rip-up clasts. It also leaves behind sediments with a chaotic character, exhibiting poor sorting and various grain sizes. This facies is located proximal to the sediment source. Facies B is composed of uniformly laminated fine to very fine sand grains. Facies C is composed of highly laminated interbedded very fine grain sand to silty clays; it often exhibits soft sediment and water escape textures. Facies D has the lowest energy, with thin to very thinly bedded clay-rich silts. These lithofacies were then integrated with the FFI-to-BVI ratio to identify formation-pressure testing points in each fluid-bearing sand. The FFI-to-BVI ratio was used as an index for rock quality classification. The results showed that Facies B yielded 100% acceptable pressure tests without any tight test. When the ratio of FFI to BVI increases, the measured mobility from formation testing is also larger. On the other hand, Facies A and D showed the poorest rock quality for fluid flow, where all the pressure tests were tight.
The integration of the Bouma sequence classification with advanced log interpretation was vital for the successful implementation of the formation pressure-testing program. The importance and contribution of lithofacies are typically ignored when attempting to optimize formation pressure depths. However, this paper provides a step forward to overcome such challenges and a new direction to assess rock quality for formation pressure testing.
Balliet, Ron (Halliburton) | Khan, Waqar Ahmad (Halliburton) | Medina, Rojelio (Halliburton) | Galford, Jim (Halliburton) | Quintero, Luis (Halliburton) | Chakraborty, Diptaroop (Halliburton) | Jambunathan, Venkat (Halliburton) | Gonnell, Tony (Cobalt International Energy Inc.) | Ramakrishna, Sandeep (Halliburton) | Bargas, Connie L. (Cobalt International Energy Inc.) | Murphy, Ryan T. (Cobalt International Energy Inc.) | Saller, Arthur H. (Cobalt International Energy Inc.)
The oil and gas potential of the pre-salt carbonates of Brazil and West Africa has been the focus of significant recent exploration interest. The primary oil targets within the Kwanza basin of West Africa are complex pre-salt carbonate reservoirs. In discovery wells, hydrocarbon fluid identification, porosity characterization, and permeability are necessary for robust resource estimates and as input data for a strategy to determine intervals for drill stem tests.
The challenges to formation evaluation, including hydrocarbon identification, are multifold. Complex distributions of pore geometries and reservoir quality are present in the formations. Light-medium native oil identification is complicated by oil-based mud filtrate invasion. The accurate determination of fluid volumes and permeability in this challenging environment requires a solution with a higher level of reservoir understanding.
A suite of advanced logging sensors, in addition to conventional measurements, are used to acquire a significant body of data for analysis, comparison, and calibration to laboratory fluid and core measurements. The gradient and samples obtained from formation testing are crucial in determining the thickness, quality, and connectivity of the hydrocarbon zone and, in turn, the commercial feasibility of the well. In the pre-salt play, where rig costs can exceed 1 million USD per day, the ability to focus data collection on zones that will yield good formation tests is of significant value to the asset operator.
This paper outlines a method of facies classification based on nuclear magnetic resonance (NMR) data. This method, when combined with other log data, has shown encouraging results in terms of identifying facies which have a high probability of yielding tight tests. These intervals may be avoided to improve the overall efficiency of the pressure and sampling program. The method presented uses an integrated workflow, developed by the operator and service company, and enables a significant savings in rig time and, subsequently, overall formation evaluation cost while still acquiring critical information.
Abstract Siliciclastic turbidite lobes and channels are known to exhibit varying degrees of architectural complexity. Understanding the elements that contribute to this complexity is the key to optimizing drilling targets, completions designs and long-term production. Several methods for 3D reservoir modelling based on seismic and electromagnetic (EM) data are available that are often complemented with outcrop, core and well log data studies. This paper explores an ultra-deep 3D EM inversion process during real-time drilling and how it can enhance the reservoir understanding beyond the existing approaches. The new generation of ultra-deep triaxial EM logging tools provide full-tensor, multi-component data with large depths of detection, allowing a range of geophysical inversion processing techniques to be implemented. A Gauss-Newton-based 3D inversion using semi-structured meshing was adapted to support real-time inversion of ultra-deep EM data while drilling. This 3D processing methodology provides more accurate imaging of the 3D architectural elements of the reservoir compared to earlier independent up-down, right-left imaging using 1D and 2D processing methods. This technology was trialed in multiple wells in the Heimdal Formation, a siliciclastic Palaeocene reservoir in the North Sea. The Heimdal Fm. sandstones are generally considered to be of excellent reservoir quality, deposited through many turbiditic pulses of variable energy. The presence of thin intra-reservoir shales, fine-grained sands, heterolithic zones and calcite-cemented intervals add architectural complexity to the reservoir and subsequently impacts the fluid flow within the sands. These features are responsible for heterogeneities that create tortuosity in the reservoir. When combined with more than a decade of production, they have caused significant localized movement of oil-water and gas-oil contacts. Ultra-deep 3D EM measurements have sensitivity to both rock and fluid properties within the EM field volume. They can, therefore, be applied to mapping both the internal reservoir structure and the oil-water contacts in the field. The enhanced imaging provided by the 3D inversion technology has allowed the interpretation of what appears to be laterally stacked turbidite channel fill deposits within a cross-axial amalgamated reservoir section. Accurate imaging of these elements has provided strong evidence of this depositional mechanism for the first time and added structural control in an area with little or no seismic signal.
Jiang, Tianmin (Schlumberger) | Gendur, Jason (Schlumberger) | Chen, Li (Schlumberger) | Xu, Weixin (Schlumberger) | Shan, Dan (Schlumberger) | Hall, Tom (TALOS Energy) | Wilkinson, Tim (TALOS Energy) | Winkelman, Ben (TALOS Energy) | Nwosu, Nnadozie (Schlumberger) | Cañas, Jesus Alberta (Schlumberger) | Hayden, Ron (Schlumberger)
ABSTRACT A novel integrated workflow using Nuclear Magnetic Resonance (NMR) data is developed to evaluate sand reservoirs in deepwater Gulf of Mexico. Accurate characterization of the reservoir properties is the key to predict the formation producibility. Traditional interpretation methods based on Triple-Combo logs (density, neutron, resistivity and gamma ray) have been widely used to characterize clastic formations to provide cost-effective answers of lithology, porosity, saturation and permeability. Nevertheless, zones with fine grained rock texture or clay-rich thin beds represent low resistivity, causing net-to-gross estimation often pessimistic. Grain size variation and clay distribution also affect the vertical permeability and connectivity. Moreover, the traditional methods cannot provide other important quantities of interest such as reservoir properties, sand facies and reservoir quality indicators. The new approach incorporates modern techniques of NMR factor analysis and fluid substitution to fully characterize the formations byidentifying fluid types, evaluating clay distribution, quantifying porosity, saturation and permeability, analyzing fluid facies from NMR factor analysis for rock typing to separate shale, clean sand and laminated sand intervals, computing grain size distribution from a simulated 100% water-filled formation using NMR fluid substitution, evaluating reservoir quality and producibility based on the reservoir properties estimated from 1) to 3). In this paper, we demonstrate the successful application of the proposed workflow to the wells in deepwater Gulf of Mexico. Interpretation from case studies are presented using wireline NMR data integrated with tri-axial resistivity, borehole image, formation testing and core analysis data. The results provide more accurate reservoir properties for better reservoir quality characterization. INTRODUCTION The Gulf of Mexico (GOM) is a major petroleum-producing area in offshore operations of considerable importance. This provided opportunities for oil and gas production, but also brought new challenges for reservoir characterization. Traditional interpretation methods based on Triple-Combo (TCOM) logs including density, neutron, resistivity and gamma ray, have been widely used to characterize clastic formations to provide cost-effective answers of lithology, porosity, saturation and permeability. Deepwater turbidite deposit formed as a result of turbidity current in grain size variations where clay distribution can affect the vertical permeability and connectivity. Zones with fine grained rock texture or thin clay-rich beds suppress resistivity, due to the parallel resistivity effect of conductive shale layers within the resistive hydrocarbon-bearing sand laminations. Net-to-gross estimation in these formations can be pessimistic when using conventional analysis (CA) with standard resistivity measurements from TCOM data. Moreover, traditional methods can be compromised in other important quantities such as clay distribution and reservoir quality.