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Collaborating Authors
Dynamic Characterization of Different Reservoir Stacked Patterns for a Giant Carbonate Reservoir in Middle East
Yong, Li (Research Institute of Petroleum Exploration and Development, PetroChina) | Qi, Zhang (Research Institute of Petroleum Exploration and Development, PetroChina) | Chenji, Wei (Research Institute of Petroleum Exploration and Development, PetroChina) | Benbiao, Song (Research Institute of Petroleum Exploration and Development, PetroChina)
Abstract Understanding reservoir stacked styles is critical for a successful water injection in a carbonate reservoir. Especially for the giant carbonate reservoirs, different regions with different reservoir architecture stacked patterns developed may be applied to different development options. So reservoir stacked pattern characterization are especially important for the giant carbonate reservoir. This paper propose a systematic method of dynamic characterization on different reservoir stacked pattern, which applied to a giant carbonate reservoir in Middle East. This paper focuses on a giant carbonate reservoir, which is a Cretaceous carbonate reservoir in Middle East, with conducting waterflooding pilots currently. Thief zones are developed in this reservoir. Four patterns of reservoir architecture stacked style are classified based on geological study. Then the proposed systematic dynamic characterization method are applied, which mainly based on the dynamic data analysis (production data, formation test pressure, production logging test data etc.) and reservoir engineering methods (material balance analysis, well test analysis). Finally, distribution of the four patterns are mapped combined geological and dynamic understanding. The four patterns have distinctive geological features and dynamic characteristics. For example, pattern A are mostly composed by good quality reservoirs with high production rate for most intervals. Pressure gradient has almost no change from top to bottom, and well test log-log curve shows typical homogeneous reservoir characteristics. And OOIP of pattern A well is large with drainage radius mostly larger than 900m. While from pattern A to pattern D, poor reservoir and barriers increase, which change from massive reservoir to multi-layered reservoir. For pattern D, pressure gradient are separated as zigzag curve, and boundary response is clearly shown in well test log-log curve. Well OOIP and well drainage radius become smaller. After systematic dynamic characterization, reservoir architectures of all wells are calibrated and validated with geological results, and lateral distribution of architecture patterns are mapped. This paper offers a case study of systematic dynamic characterization of reservoir architectures for carbonate reservoirs. And understanding of reservoir architectures is much valuable for the next development options decision, which means different waterflooding well pattern, well type etc. are applied to different reservoir architectures. It also provides a methodology and reference case for engineers and geologists to investigate on other similar fields.
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.37)
Reservoir Management of Gas Condensate Fields: Case Studies from the Niger Delta
Adeyinka, Adeboye Kayode (Chevron Nigeria Limited) | Eruvbetine, Obor (Chevron Nigeria Limited) | Jaja, Adagogo (Chevron Nigeria Limited) | Aladesulu, Adeniyi (Chevron Nigeria Limited) | Udeh, Uchenna (Chevron Nigeria Limited) | Olayomi, Joseph (Chevron Nigeria Limited)
Abstract Natural gas has been widely touted as a transition fuel due to its lower greenhouse gas emissions when compared to other fossil fuels such as coal and oil, serving as a bridge fuel between the fossil fuels and the renewables. This has made gas condensate reservoirs (GCRs) a crucial resource in meeting the increasing demand for cleaner, more affordable energy for the energy transition. GCRs while producing gas also deliver valuable condensate liquid that could pose production challenges. An example is condensate banking around the wellbore which reduces overall well productivity. Furthermore, GCRs also require more rigorous pressure-volume-temperature (PVT) modeling to cover the producing lifecycle of the reservoir and understand how much of the condensate will be produced to the surface. Resolving these additional complexities with GCRs requires proper surveillance data acquisition, PVT analysis, reservoir simulation to improve reservoir understanding and integrated asset models to bring all the knowledge together and capture flow interactions. Data such as fluid lab tests, reservoir pressure trend, pressure transient testing and production tests are analyzed; giving insights into fluid, reservoir, and near-wellbore properties. Gas wells are typically online for extended periods to meet daily contract quantities (DCQ). This poses a challenge to the acceptable duration of pressure build-up (PBU) tests. For this, a review of some opportunistic extended build-up tests that were done during a scheduled Turn-Around Maintenance that allowed a deeper radius of investigation into the reservoirs is presented. This text builds on existing literature on the management of gas condensate reservoirs highlighting lessons learnt and best practices while using case studies from some Niger Delta reservoirs. The case studies presented cover a wide range of some reservoir parameters that impact expected ultimate recovery such as permeability and condensate-gas-ratio (CGR) while discussing field practices and results.
- Asia > Middle East (1.00)
- Africa > Nigeria > Niger Delta (0.61)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Gas-condensate reservoirs (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Management > Professionalism, Training, and Education (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Petroleum reservoir simulation is the application of software designed to model fluid flow in petroleum reservoirs. I first encountered reservoir simulation while working on a project to store solar energy in an aquifer. During the first 3 years of my career, I performed a model study of a relatively small oil reservoir, reviewed the status of naturally fractured reservoir simulators, conducted an analysis of multidimensional numerical dispersion, evaluated the feasibility of developing geopressured/geothermal reservoirs, and compared the relative merits of different chemical flood processes. I thus found that a career in reservoir simulation would provide interesting challenges in a wide range of applications. The process of petroleum reservoir simulation is an aspect of reservoir management.
Seismic Reflection Characteristics of Deeply-Buried, Layered, Karstic Carbonate Reservoir Strata
Peiling, Ma (BGP, CNPC) | Dong, Lv (BGP, CNPC) | Yonglei, Liu (BGP, CNPC) | Haiting, An (BGP, CNPC) | Xingyin, Xiong (BGP, CNPC) | Qiang, Xian (BGP, CNPC) | Zujun, Wang (BGP, CNPC) | Xiangzhou, Zeng (BGP, CNPC)
Based on a weathering crust on the unconformity, erosion likely This paper describes a reservoir study of deeply-buried occurred within 12.8 Ma (Yu et al., 2011). Contemporaneous carbonate karstic rocks in the Tazhong oilfield of the Tarim with uplift and erosion, the region experienced multiphase basin. We first classify seismic reflective characteristics into fracturing, cracking and burial dissolution transformation, in different seismic facies. We then present different seismic which fractures and fracture networks developed to form the reflection types using drilling material,fracture prediction main karst fluid channels, which eventually formed the and seismic attribute analysis, We finally study the current reservoir characteristics.
- Geology > Geological Subdiscipline (0.94)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.37)
The process of post-stack inversion is routinely carried out to obtain acoustic impedance (AI) from seismic data and, hence reservoir properties in the target oil field. In this study, we have been describing characterization of a Mid-Cretaceous reservoir using two seismic inversion techniques which have been applied to the main oil bearing complex reservoir in this giant field. These two methods were implemented as an approach to predict the reservoir properties and the reservoir features such as the complex geometries and connectivity patterns, with focusing on benefits and uncertainties between these two techniques. Analysis of post-stack seismic inversion for various reservoir properties estimation is generally applied for reservoir characterization. The two methods have been interrogated and successfully applied on a heterogeneous carbonate reservoir. Model based inversion has been extensively applied on many case studies and has produced superior results on other techniques. The second methodology, linear programming sparse spike (LPSS) inversion is also much used for reservoir characterization with promising results. In this case study, high resolution 3-D seismic data was used with the data of twenty well logs. First of all, building rock physics model was performed to relate the acoustic properties and the reservoir properties, also to provide a better understating of the main elements that control the seismic amplitude in this heterogeneous carbonate reservoir. Then, both techniques were applied across the 3-D seismic data of the field, using an estimated wavelet and low frequency model. The rock physics model demonstrated a clear linear relationship between the porosity and acoustic properties with high correlation in the Mishrif interval. The Mishrif reservoir is typically bioclastic-detrital limestone subzones with heterogeneous quality. Consequently, a various reservoir properties observed using these two inversion techniques, also the well data demonstrated a strong correlation at the well locations. Model based and linear programming sparse spike methods have been applied to the Mishrif reservoir over the field. The results of both techniques were compared with computed acoustic impedance logs on the basis of blind well tests, to identify the subzones of the Mishrif reservoir. The qualitatively and quantitatively analysis of inversion results suggest that model based inversion is generally better than those from the LPSS inversion, in terms of reservoir properties and the distribution of carbonate tidal channels. It can be concluded that the acoustic properties of the Mishrif reservoir, derived from model based inversion, helped to identify the reservoir features such as channel fairways and high quality facies such as reef and clinoforms. This research focuses on how the comparison of inversion methods, used to improve the characterization of the Mishrif reservoir in this giant oil field, and also for similar heterogeneous carbonate reservoirs.
- Asia > Middle East > Iraq > Basra Governorate (0.69)
- North America > United States (0.67)
- Research Report > New Finding (0.54)
- Research Report > Experimental Study (0.34)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Geological Subdiscipline > Geomechanics (0.87)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.67)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- South America > Brazil > Campos Basin (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Blackfoot Field > Blackfoot Reserv. 10 Th 14-19-22-23 Well (0.99)
- Asia > Middle East > Iraq > Maysan Governorate > Arabian Basin > Widyan Basin > Zagros Orogenic Belt > Dezful Embayment > Missan Field > Jabal Fauqui Field (0.99)
- (7 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Carbonate reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)