Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands

Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that framework, we use velocity and porosity measurements on unconsolidated, dry, and clean sands. Three pressure- and three porosity-dependent rock-physics models are applied to the data to examine relationships among the inputs. As with any Bayesian formulation, we define a prior model and calculate the likelihood in order to evaluate the posterior. With relatively few inputs to consider for each rock-physics model, we found that sampling the posterior exhaustively to be convenient. The results of the Bayesian analyses are multivariate histograms that indicate most likely values of the input parameters given the data to which the rock-physics model was fit. When the Bayesian procedure is repeated many times for the same data, but with different prior models, correlations emerged among the input parameters in a rock-physics model. These correlations were not known previously. Implications, for the pressure- and porosity-dependent models examined here, are that these correlations should be utilized when applying these models to other relevant data sets. Furthermore, additional rock-physics models should be examined similarly to determine any potential correlations in their inputs. These correlations can then be taken advantage of in forward and inverse problems posed in reservoir characterization.

Direct shear-wave seismic survey in Sanhu area, Qaidam Basin, west China

The formation containing shallow gas clouds poses a major challenge for conventional P-wave seismic surveys in the Sanhu area, Qaidam Basin, west China, as it dramatically attenuates seismic P-waves, resulting in high uncertainty in the subsurface structure and complexity in reservoir characterization. To address this issue, we proposed a workflow of direct shear-wave seismic (S-S) surveys. This is because the shear wave is not significantly affected by the pore fluid. Our workflow includes acquisition, processing, and interpretation in calibration with conventional P-wave seismic data to obtain improved subsurface structure images and reservoir characterization. To procure a good S-wave seismic image, several key techniques were applied: (1) a newly developed S-wave vibrator, one of the most powerful such vibrators in the world, was used to send a strong S-wave into the subsurface; (2) the acquired 9C S-S data sets initially were rotated into SH-SH and SV-SV components and subsequently were rotated into fast and slow S-wave components; and (3) a surface-wave inversion technique was applied to obtain the near-surface shear-wave velocity, used for static correction. As expected, the S-wave data were not affected by the gas clouds. This allowed us to map the subsurface structures with stronger confidence than with the P-wave data. Such S-wave data materialize into similar frequency spectra as P-wave data with a better signal-to-noise ratio. Seismic attributes were also applied to the S-wave data sets. This resulted in clearly visible geologic features that were invisible in the P-wave data.

Characterization of Mishrif Formation Reservoir in Amara Oil Field, Southeast Iraq, Using Geophysical Well-logging

Reservoir characterization requires reliable knowledge of certain fundamental properties of the reservoir. These properties can be defined or at least inferred by log measurements, including porosity, resistivity, volume of shale, lithology, water saturation, and permeability of oil or gas. The current research is an estimate of the reservoir characteristics of Mishrif Formation in Amara Oil Field, particularly well AM-1, in south eastern Iraq. Mishrif Formation (Cenomanin-Early Touronin) is considered as the prime reservoir in Amara Oil Field. The Formation is divided into three reservoir units (MA, MB, MC). The unit MB is divided into two secondary units (MB1, MB2) while the unit MC is also divided into two secondary units (MC1, MC2). Using Geoframe software, the available well log images (sonic, density, neutron, gamma ray, spontaneous potential, and resistivity logs) were digitized and updated. Petrophysical properties, such as porosity, saturation of water, saturation of hydrocarbon, etc. were calculated and explained. The total porosity was measured using the density and neutron log, and then corrected to measure the effective porosity by the volume content of clay. Neutron -density cross-plot showed that Mishrif Formation lithology consists predominantly of limestone. The reservoir water resistivity (Rw) values of the Formation were calculated using Pickett-Plot method.

Structural Reservoir Characterization of Akkas Gas Field, Western Iraq: Implications for Hydrocarbon Recovery

Akkas Field is a structural trap with a sandstone reservoir that contains proven gas condensate. The field is a faulted anticline that consists of the Ordovician Khabour Formation. The objective of this research is to use structural reservoir characterization for hydrocarbon recovery. The stratigraphic sequence of the Silurian and older strata was subjected to an uplift that developed a gentle NW-SE trending anticline. The uplifting and folding events developed micro-fractures represented by tension cracks. These microfractures, whether they are outer arc or release fractures, are parallel to the hinge line of the anticline and perpendicular to the bedding planes. The brittle sandstone layers of the reservoir are interbedded with ductile units of shale. The sandstone layers accommodate the formation of micro fractures that play a major role to increase the secondary porosity. The gas and condensate have been stored mainly through the micro fractures. Two types of drilling have been used for experimental gas production, vertical and horizontal. Horizontal drilling was parallel to both hinge line of the anticline and micro fracture surfaces that was conducted and doubled the gas production of the vertical well multiple times. However, if used the third type of drilling, directional, that is perpendicular to the hinge line and parallel to the beddings of both flanks of the anticline gas production will increase more than the horizontal drilling. The directional drilling will become perpendicular to the fracture surfaces and allow the gas and the condensate to flow into the well from all directions. Additionally, it will reduce the effect of both semi – liquid hydrocarbon condensate and vertical sediment barriers.

Model Regeneration Scheme using a Deep Learning Algorithm for Reliable Uncertainty Quantification of Channel Reservoirs

Reservoir characterization is one of the essential procedures for decision makings. However, conventional inversion methods of history matching have several inevitable issues of losing geological information and poor performances when it is applied to channel reservoirs. Therefore, we propose a model regeneration scheme for reliable uncertainty quantification of channel reservoirs without conventional model inversion methods. The proposed method consists of three parts: feature extraction, model selection, and model generation. In the feature extraction part, drainage area localization and discrete cosine transform are adopted for channel feature extraction in near-wellbore area. In the model selection part, K-means clustering and an ensemble ranking method are utilized to select models that have similar characteristics to a true reservoir. In the last part, deep convolutional generative adversarial networks (DCGAN) and transfer learning are applied to generate new models similar to the selected models. After the generation, we repeat the model selection process to select final models from the selected and the generated models. We utilize these final models to quantify uncertainty of a channel reservoir by predicting their future productions. After appling the proposed scheme to 3 different channel fields, it provides reliable models for production forecasts with reduced uncertainty. The analyses show that the scheme can effectively characterize channel features and increase a probability of existence of models similar to a true model.