Academic literature on the topic 'Petrophysical analysis'

This study presents petrophysics analysis results from two wells located in the Arafura Basin. The analysis carried out to evaluate the reservoir characterization and its relationship to the stratigraphic sequence based on log data from the Koba-1 and Barakan-1 Wells. The stratigraphy correlation section of two wells depicts that in the Cretaceous series a transgression-regression cycle. The petrophysical parameters to be calculated are the shale volume and porosity. The analysis shows that there is a relationship between stratigraphic sequences and petrophysical properties. In the study area, shale volumes used to make complete rock profiles in wells assisted by biostratigraphic data, cutting descriptions, and core descriptions. At the same time, porosity shows a conformity pattern with the transgression-regression cycle.Keywords: petrophysics, reservoir characterization, Cretaceous, transgressive-regressive cycle

We have developed a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geologic data. In this framework, probabilistic geologic modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then uses petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-squares framework to capture and integrate information related to geophysical, petrophysical, and geologic data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geologic field data, we have generated a set of geologic models, which we used to derive a probabilistic geologic model. In this synthetic case study, we found that the integration of geologic information and petrophysical constraints in geophysical joint inversion could reduce uncertainty and improve imaging. In particular, the use of petrophysical constraints retrieves sharper boundaries and better reproduces the statistics of the observed petrophysical measurements. The integration of probabilistic geologic modeling permits more accurate retrieval of model geometry, and it better constrains the solution while still satisfying the statistics derived from geologic data. The analysis of statistical indicators at each step of the workflow indicates that (1) the inversion methodology is effective when applied to complex geology and (2) the integration of prior information and constraints from geology and petrophysics significantly improves the inversion results while decreasing uncertainty. Finally, the analysis of uncertainty to the integration of the conditioned petrophysical constraints also indicates that, for this example, the best results are obtained for joint inversion using petrophysical constraints spatially conditioned by geologic modeling.

SUMMARY In a previous paper, we introduced a framework for carrying out petrophysically and geologically guided geophysical inversions. In that framework, petrophysical and geological information is modelled with a Gaussian mixture model (GMM). In the inversion, the GMM serves as a prior for the geophysical model. The formulation and applications were confined to problems in which a single physical property model was sought, and a single geophysical data set was available. In this paper, we extend that framework to jointly invert multiple geophysical data sets that depend on multiple physical properties. The petrophysical and geological information is used to couple geophysical surveys that, otherwise, rely on independent physics. This requires advancements in two areas. First, an extension from a univariate to a multivariate analysis of the petrophysical data, and their inclusion within the inverse problem, is necessary. Secondly, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information. To illustrate the efficacy of our approach and the advantages of carrying out multi-physics inversions coupled with petrophysical and geological information, we invert synthetic gravity and magnetic data associated with a kimberlite deposit. The kimberlite pipe contains two distinct facies embedded in a host rock. Inverting the data sets individually, even with petrophysical information, leads to a binary geological model: background or undetermined kimberlite. A multi-physics inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe. Through this example, we also highlight the capabilities of our framework to work with interpretive geological assumptions when minimal quantitative information is available. In those cases, the dynamic updates of the GMM allow us to perform multi-physics inversions by learning a petrophysical model.

Reservoir characterization involves computing various petrophysical parameters and defining them in terms of their quantity and quality so as to ascertain the yield of the reservoir. Petrophysical well log and core data were integrated to analyze the reservoir characteristics of Explorer field, Offshore, Niger Delta using three wells. The study entails determination of the lithology, shale volume (Vsh), porosity (Φ), permeability (K), fluid saturation and cross plotting of petrophysical and core values at specific intervals to know their level of correlation. The analysis identified twelve hydrocarbon-bearing reservoir from three different wells. Average permeability value of the reservoir is 20, 0140md while porosity value range between 18% to 39%. Fluid type defined in the reservoirs on the basis of neutron/density log signature were basically water, oil and gas, low water saturation values ranging from 2.9% to 46% in Explorer wells indicate high hydrocarbon saturation. The Pearson Correlation Coefficient and Regression Equation gave a significant relationship between petrophysical derived data and core data. Scatter plot of petrophysical gamma ray values versus core gamma ray values gave an approximate linear relationship with correlation coefficient values of 0.6642, 0.9831 and 0.3261. Crossplot of petrophysical density values and core density values revealed that there is a strong linear relationship between the two data set with correlation coefficient values of 0.7581, 0.9872 and 0.3557, and the regression equation confirmed the relationship between the two data set. Also the scatter plot of petrophysical porosity density values versus core porosity density values revealed a strong linear relationship between the two data set with correlation coefficient values of 0.7608 and 0.9849, the regression equation confirmed this also. Crossplot of petrophysical porosity density values versus core porosity density values in Well 3 gave a very weak correlation coefficient values of 0.3261 and 0.3557 with a negative slope. The petrophysical properties of the reservoirs in Explorer Well showed that they contain hydrocarbon in commercial quantity and the cross plot of the petrophysical and core values showed direct relationship in most of the wells.

Hydrocarbon reservoirs are characterised by integrating seismic, well-log and petrophysical information, which are dissimilar in spatial distribution, scale and relationship to reservoir properties. Well logs are essential for amplitude versus offset (AVO) modelling and seismic inversion. The usability of well logs can be determined during wavelet estimation, seismic-to-well ties, background model building, property distribution for inversion, deriving probability density functions and variograms, offset-to-angle conversion of seismic data, and many other processes. For the implementation of seismic inversion workflows, accurate and geologically corrected compressional-sonic, shear-sonic and density logs are necessary. Preparing the logs for quantitative interpretation becomes challenging in a real-field environment because of bad borehole conditions including washouts, uncalibrated and variability of logging tools, invasion effects, missing shear logs and change of borehole size. Conventional petrophysical analysis is usually restricted to the reservoir interval, the calculation of reservoir versus non-reservoir (including sands or shales), and log corrections for smaller intervals; in contrast, seismic petrophysics encompasses the entire geological interval, calculates the volume of multi-minerals, incorporates boundaries between non-reservoir and reservoir, and often includes the prediction of missing compressional and shear-sonic for AVO analysis. A detailed seismic petrophysics analysis was performed for amplitude versus angle (AVA) modelling and attributes analysis. To perform the AVA modelling, a series of forward models in association with rock physics modelled fluid-substituted logs were developed, and associated seismic responses for various pore fluids and rock types studied. The results reveal that synthetic seismic responses together with the AVA analysis show changes for various lithologies. AVA attributes analysis show trends in generated synthetic seismic responses for various fluid-substituted and porosity logs. Reservoir modelling and fluid substitution increases understanding of the observed seismic response. This paper describes detailed data analysis using various techniques to confirm the rock model for petrophysical evaluation, rock physics modelling, AVA analysis, pre-stack seismic inversion, and the scenario modelling applied to the study of an oil field in Australia.

In conducting petrophysics analysis, there are many methods on each property. Therefore, it is necessary to determine the exact method on each petrophysical property suitable for application in the field of research in order to avoid irregularities at the time of interpretation. The petrophysical property consists of volume shale, porosity, water saturation, etc. This research used six well data named FBT01, FBT02, FBT03, FBT04, FBT05, and FBT06 and also assisted with core data contained in FBT03. Core data used as a reference in petrophysical analysis because it was considered to have represented or closed to the actual reservoir conditions in the field. The area in this research was in Talangakar Formation, "FBT" Field, South Sumatra Basin. The most suited volume shale method for “FBT” field condition was gamma ray-neutron-density method by seeing its photo core and lithology. As for the effective porosity, the most suited method for the field was neutron-density-sonic method by its core. Oil-water contact was useful to determine the hydrocarbon reserves. Oil-water contact was obtained at a depth of 2277.5 feet on FBT01, 2226.5 feet on FBT02, 2312.5 feet on FBT03, 2331 feet on FBT04, 2296 feet on FBT05, and 2283.5 feet on FBT06. The oil-water contact depth differences at Talangakar formation in FBT field caused by structure in subsurface.

The complexity of shale formation interpretation requires an accurate evaluation of a detailed petrophysical model in association with the analysis of the geomechanical properties. Mineralogy plays an important role in controlling shale’s mechanical properties, among which one of the most problematic parameters to establish is the Biot’s coefficient. Although, this parameter is necessary to determine the magnitude of the effective stresses acting in the reservoir, it is not included in the standard protocols used in Poland. This paper presents a comprehensive petrophysical and geomechanical evaluation of the unconventional reservoirs of lower Paleozoic age formation: lower Silurian and Ordovician deposits located in the onshore part of the Baltic Basin (Poland). In this study, the Biot’s coefficient from well-log data was calculated. Initially, a calibrated rock-physics model was derived to provide a set of relationships between the elastic and petrophysical properties. Based on an accurate, calibrated petrophysical model, the effective bulk modulus along with the Biot’s coefficient and horizontal stresses were calculated. Ultimately, the tectonic regime was determined. Using full-waveform sonic data analysis, the horizontal anisotropy was estimated. The directions of maximum and minimum horizontal stress were established based on several X-tended Range Micro Imager images of breakout structures and drilling-induced fractures.