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![Figure 2. Integrated reservoir modeling workflow [3].](publication/324918146/figure/fig2/AS:622208980373505@1525357589577/Integrated-reservoir-modeling-workflow-3_Q320.jpg)
![Figure 4. Sequential gaussian simulation flowchart [9].](publication/324918146/figure/fig4/AS:622208980357127@1525357589645/Sequential-gaussian-simulation-flowchart-9_Q320.jpg)
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This paper presents a gas-water two-phaseLaminar-Inertial-Turbulent (LIT) flow equation for watered-out gas wells, which can be solved through the incorporation of daily production, gas-water relative permeability and PVT data, and then QAOF and formation factor (Kh) are obtained. Field case study in Long Wang Miao (LWM) water-drive gas reservoir s...
Citations
... Any static modeling is a technique in which reservoir characteristics observed from outcrops and core samples, well data, and seismic data are incorporated to construct a model representing the subsurface (Oloo & Xie, 2018). Reservoir modeling involves developing the reservoir's structural design, modeling stratigraphy, modeling facies within each stratigraphic layer, and modeling petrophysical attributes depending on the facies (Oloo & Xie, 2018). ...
... Any static modeling is a technique in which reservoir characteristics observed from outcrops and core samples, well data, and seismic data are incorporated to construct a model representing the subsurface (Oloo & Xie, 2018). Reservoir modeling involves developing the reservoir's structural design, modeling stratigraphy, modeling facies within each stratigraphic layer, and modeling petrophysical attributes depending on the facies (Oloo & Xie, 2018). There are numerous advantages to static modeling. ...
... There are numerous advantages to static modeling. Firstly, it allows the representation of the different sets of data in a unique format (Oloo & Xie, 2018). Additionally, the static model can act as an input for the dynamic simulation, helps to estimate the original hydrocarbon in place, analyze the uncertainties, and optimally design the well location (Oloo & Xie, 2018). ...
Reservoir characterization is a critical factor in reservoir management, optimizing production, and enhancing recovery. Lack of understanding of reservoir characteristics is one of the leading causes of mistakes in constructing the static reservoir model. Understanding the three-dimensional variability of any reservoir static model requires an understanding of its depositional environment. This paper is about improving the geostatistical resolution of two or three-dimensional digital recordings by using outcrop analogs to help with small and large-scale reservoir modeling challenges. The investigation was conducted on one carbonate outcrop in Ras Al Khaimah, UAE, namely the Wadi Ghalilah formations. Initially, easily spottable characteristics of the reservoir formation were noted. Following that, the outcrop was categorized into various units. Each observed zone has provided details about the formation characteristics, the fracture and matrix network, and the lithology. Easily noticeable color changes were observed. Additionally, fossil impressions and iron-stained formations were observed at the outcrop. The layers and their thickness, matrix network, fracture network, their intensity, and orientation were observed at small-scale and large-scale. The formation characteristics hinted at the porosity and permeability of the outcrop. The observed formation characteristics and geometry properties were inputted into the static model, and subsequently, data was entered into the dynamic model to minimize the uncertainties in both the reservoir models. The areal sweep efficiency is expected to be fine during waterflooding in a small, patterned reservoir whereas water injection doesn’t appear to be a suitable option at Wasia and Thamama outcrops due to the presence of seal layers and vertical heterogeneities.
... Only the area very close to the simulated well reproduces some variability being this area geometrically strongly influenced by the shape of the variogram (Fig. 8D). Actually, the adopted variogram strongly influenced also the Base Case results with both algorithms as commonly observed in the other modelling examples (Asghari et al., 2009;Oloo and Xie, 2018). The variogram adopted in the modelling of the LFF reservoir was calculated from the analysis of the HHC distribution derived from the well logs. ...
Despite data coming from fully developed oil fields are fundamental to validate reservoir models, they are rarely publishable because of confidentiality. In this work, we benefit from an exceptionally dense public dataset represented by 43 wells logs drilled in a carbonate-heavy oil-rich reservoir of the Majella Mountain (Central Italy) over a grid of 200 m x 200 m, with depths in the range of 90-200 m. We tested different modeling solutions, to assess the best modelling approach that fits the available data on hydrocarbon distribution. Both deterministic (using Kriging) and stochastic simulations (using Sequential Gaussian Simulation- SGS) were tested. The role of the variogram resulted as a fundamental parameter for both simulation methods. Wells-derived variogram agrees with the size and orientation of carbonate dunes that drive the hydrocarbon distribution highlighting the importance of an accurate depositional model. Kriging was faster in terms of computation time and better in maintaining the lateral continuity of the layers, however, it excessively smoothed the results. SGS gave a better distribution of computed values but the lateral continuity was not preserved. By adding a main vertical trend derived from the upscaled logs to the SGS, results become more accurate. We then applied the best model to estimate the total volume of hydrocarbon in place in an already exploited area starting from just one well. The computed reserves show a good fit with historical production data demonstrating the reliability of the model.
A good understanding of reservoir’s quality is crucial to hydrocarbon recovery optimization, especially in brown fields. The FORT Field is a brown field in Niger Delta with most of its wells clustered around a region. This study was therefore carried out to understand the reservoir quality distribution away from well locations, for a better recovery plan. The dataset comprising of well log, biostratigraphic and seismic data was employed for the study. A standard interpretation technique using Petrel software was adopted. Depositional sequences were identified and dated using the well logs and biostratigraphic data. Reservoirs within the sequences were identified, and the depositional environment of the reservoirs was inferred from gamma ray log motifs. Porosity, permeability and water saturation were calculated using established petrophysical log relationships. The reservoir models were generated using sequential indicator and sequential Gaussian simulation algorithms. The quality of the reservoirs was assessed based on the generated models. The result of the study showed that the FORT Field is underlain by three depositional sequences, bounded by 10.60 Ma, 10.35 Ma, 8.60 Ma and 6.70 Ma sequence boundaries. Three hydrocarbon reservoirs were identified in the lowstand systems tracts of the two shallower sequences. The environment of deposition of the sediments was inferred to range from marginal marine to shallow marine environment. The identified reservoirs were interpreted to be composed of shoreface, tidal channel and distributary channel sands. The structural framework of the study area is made up of seven major faults which run in the east–west direction and are connected to the reservoirs. The result of the petrophysical models showed porosity range of 0.1–0.35, permeability range of 300–3000 mD and water saturation range of 0.09–0.99. The study concluded that the reservoirs in FORT Field have moderate to good reservoir quality.
