Estimating the lateral heterogeneity of geochemical properties of organic rich mudrocks is important for unconventional resource plays. Mature regions can rely on abundant well data to build empirical relationships and on traditional geostatistical methods to estimate properties between wells. However, well penetration in emerging plays are sparse and so these methods will not yield good results. In this case, quantitative seismic interpretation (QSI) might be helpful in estimating the desired properties. In this study, we use QSI based on a rock physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik Formation, North Slope, Alaska. A rock physics template incorporating lithology, pore fraction, kerogen fraction, and thermal maturity is constructed and validated using well data. The template clearly shows that the inversion problem is non-unique. Inverted impedances cubes are estimated from three seismic angle gathers (near with angles between 0° and 15°, mid with angle gathers between 15° and 30°, and far with angle gathers between 30° and 45°). The inversion is done using a model-based implementation with an initial earth model derived from the seismic velocity model used in the processing phase. By combining the rock physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet spot detection. Lithological results can also be used as an input for basin and petroleum system modeling.
Increased demand for gas in the recent years has motivated Exploration companies to revisit erstwhile overlooked Miocene Biogenic gas potentials, onshore Abu Dhabi. This paper detail how advanced geophysics techniques including rock physics forward modeling and inversion has been integrated to understand the distribution of potential gas bearing zones in this complex unconventional setting where inter-well information is limited. The results integrated with understandings from other disciplines support drilling and field appraisal strategy in the area.
The Miocene Formation of interest consists of an Upper, Middle and Lower unit, with varying levels of complexities and hydrocarbon presence identified from drilled wells. We show how we integrated all available data including logs, core, fluids, cuttings, mud-gas, petrophysical and seismic information to constrain the seismic forward model and invert the seismic data to define potential for gas presence in the area. Lithologic boundaries were defined from cuttings and geologic correlations. Half-space rock properties analyses and well ties provided understanding of the seismic responses, and the geologic picks mapped accordingly. Gassmann fluid substitution were carried out using conditioned Vp, Vs and density logs to understand the sensitivity of the lithologies to different pore fluid fill including brine and different gas proportions. AVO forward modeling was also carried out to understand if gas ‘sweet spots’ may be visible from analyses of amplitudes. Rock physics plots were analysed including AI, SI, GI, PI and Lame's parameters to establish relationship to reservoir properties, and optimum discriminators of fluid, porosity and TOC were accordingly determined. The low frequency model was developed from logs, and prestack 2D seismic data up to 40° were inverted for elastic impedances. Bayesian rock type classification scheme was deployed to extract potential gas prolific areas.
Seismic rock properties analyses provided invaluable insight to the reservoir characterisation strategy for the Biogenic gas formation. The analyses showed that delineating gas presence is challenging using conventional amplitude or AVO analyses techniques. Potential for fluid optimization exists from analyses of poisson's impedance (PI) as well as extended elastic impedance at the fluid projection with reasonable certainty. Pre-stack simultaneous inversion of the seismic lines was carried out, followed by Bayesian rock type classification to identify regions of increased gas potential in areas of seismic coverage
This paper represents for the first time integrated seismic rock properties and inversion techniques are applied to delineate an unconventional Biogenic gas reservoir. The results hold potential benefit for well placement and input to distribution of reservoir properties in the geologic model. The method will be extended to analyzing the gas potential from the currently acquired mega 3D seismic over Abu Dhabi.
Rajput, Sanjeev (Petronas Carigali Sdn Bhd) | Bt Abdullah, Irmawaty (Petronas Carigali Sdn Bhd) | Roy, Amit (Petronas Carigali Sdn Bhd) | B. Khalid, Aizuddin (Petronas Carigali Sdn Bhd) | Onn, Camellia (Petronas Carigali Sdn Bhd) | Khalil, Ashraf (Petronas Carigali Sdn Bhd)
Low electrical resistivity and low contrast reservoirs (LRLC) pay zones are composed of thinly-bedded laminated layers containing hydrocarbon accumulations surrounded by non-reservoir layers indicating lack of resistivity contrast. These pay zones are difficult to be distinguished at seismic and log scale due to lower vertical and lateral resolution. Traditionally, deep-resistivity logs in LRLC zones read 0.5 to 5 ohm-m. Low contrast pay zone occurs mainly when the formation waters are fresh or having low salinity resulting in a very little resistivity contrast between oil and water zones. Major challenges imposed in LRLC reservoirs include identification, characterization, and evaluation of the hydrocarbon interval, which is usually masked by the lack of resistivity contrast between the hydrocarbon and water zones. The identification and characterization of the lowdown on resistivity pay is essential for the re-development of mature assets for improved oil recovery. This paper deals with the characterization of low resistivity hydrocarbon-bearing thinly-bedded reservoirs from a brownfield.
To unlock the hidden potential of LRLC pay sands in the offshore Sarawak Malaysia, the effective integration of subsurface disciplines including petrophysics, geology and quantitative derivatives from the seismic analysis is vital. This study covers the geological perspective of low contrast reservoirs from an offshore oil field deposited in lower coastal plain settings located within offshore Sarawak Malaysia. An improved understanding of the geological, petrophysical and geophysical parameters was achieved by adopting a holistic and multidisciplinary approach. This includes the integration of core, logs, rock physics modeled parameters, stratigraphic, depositional and lithofacies information along with stochastic inversion derivatives. Acoustic Impedance shows the facies changes in broader terms between producing and non-producing zone.
The paper quantifies rock physics parameter uncertainties for LRLC pay zones and establishes a framework for LRLC reservoir characterization. Stochastic inversion derived P-Impedance and Vp/Vs ratio are used to predict fluid and facies probabilities (
Identified LRLC reservoirs proved to be of commercial-quality and increased oil production to the extent of several hundred thousands of barrels over the years and currently producing. Rock physics modeled parameters including AI and Vp/Vs are sensitive to LRLC pay zones and their effective integration with image logs, lithofacies, and seismic inversion lead to reduce uncertainties in infill drilling programs. Geological understanding of the possibility of LRLC occurrences is required to assess oil and gas bypassed oil. Detailed geological features are clearly resolved in high-definition image logs. Low resistivity pay zones present in the main reservoir intervals can be identified by integrating the information from low gamma ray, low impedance, and low resistivity zones collectively. The results of this study show the value of integrated approaches and improvements in reservoir description from stochastic inversion into reservoir models.
The main objective of the course is to apply geophysics to petroleum engineering aspects of reservoir analysis by demonstrating how the models arrived. Several key topics will be discussed in detail including: stress analysis, rock physics, rock mechanics, and reserve estimate. The integration of multiple seismic inversion models will be described in a manner that improves communication. Students should have an existing understanding of ESP equipment and operations. Peter Bartok is an adjunct professor of geology at the University of Houston, instructor at Petroskills and a Petroleum E&P Consultant with research interests in unconventional shale resources, complex salt tectonics, rock physics and rock mechanics.
There are several specific differences between exploration geophysics and reservoir geophysics, as the term is usually intended. The reservoir geophysicist should be familiar with the usefulness and limitations of petrophysical and reservoir-engineering studies and should be able to ask intelligent questions of the experts in those fields. However, the reservoir geophysicist typically is not an expert in those areas and works with the appropriate specialists to interpret the data or to design a new experiment to solve reservoir problems. In exploration, extrapolation of well data from far outside the area of interest is often necessary, and the interpretation is required to cross faults, sequence boundaries, pressure compartments, and other discontinuities that may or may not be recognized. The interpreter resorts to analogs in the absence of hard data, and local calibration of the geophysical response is generally poor.
Swami, Vivek (CGG) | Tavares, Julio (CGG) | Pandey, Vishnu (CGG) | Nekrasova, Tatyana (CGG) | Cook, Dan (Bravo Natural Resources) | Moncayo, Jose (Bravo Natural Resources) | Yale, David (Yale Geomechanics Consulting)
In this study, a state-of-the-art seismic driven 3D geological model was built and calibrated to a petrophysical and geomechanical analysis, 1D-MEM (Mechanical Earth Model), on chosen wells within the Arkoma Basin of Oklahoma. The well information utilized in this study included basic wireline logs and core analysis, including XRD (X-Ray diffraction) data. The traditional petrophysical analysis was augmented with advanced rock physics and statistical techniques to generate the necessary logs. Hydrostatic, overburden and pore pressures were calculated with a petrophysical evaluation model. The 1D-MEMs were based on the Eaton/Olson/Blanton approach with the HTI (Horizontal Transverse Anisotropy) assumption. The 1D-MEMs were calibrated to laboratory data (triaxial tests) and field observations (mud logs, wellbore failure, frac pressures). Therefore, a very good confidence was achieved on Biot's coefficient, tectonic components, anisotropy and dynamic to static conversion factors for Young's Modulus and Poisson's Ratio. Seismic inversions were performed in different time windows and merged to generate high resolution P- and S-Impedance attributes from surface down to the target interval after careful AVO compliant gather preconditioning. A density volume estimate was calibrated to well data, accounting for different geological formations, to decouple P- and S-Wave components as a 3D volume, as well as dynamic Young's modulus (E) and Poisson's ratio (PR). Dynamic E and PR were converted to static parameters using results from 1D-MEMs; and 3D models of Biot's coefficient (α) and tectonic components were built to compute 3D fracture pressure volumes calibrated to well data. The final products were seismic-driven 3D pore pressure and fracture pressure calibrated to 1D-MEMs. The correlation between measured/estimated well logs and corresponding seismic-derived pseudo logs was more than 80%, which indicates good quality of seismic inversion results and hence 3D-MEM. Also, stress barriers, anisotropy, and brittleness indices were calculated on well scale which would help to identify best zones to place hydraulic fractures. The 3D geological model will aid in identifying sweet-spots and optimizing hydraulic fractures.
Elastic properties of unconventional rock, including gas/oil shale and tight gas sand (TGS), are crucial in hydraulic fracture modeling. The two most important rock elastic properties are Young's modulus and Poisson's ratio. These properties can be determined from sonic well logs, but the required logs (compressional and shear velocity) are not always available. These properties can be measured from plug samples using a triaxial load frame, but these tests are slow, expensive, and require an intact cylindrical sample.
An alternative is to use rock physics modeling applied to mineralogy and porosity computed from ion-milled scanning electron microscope (SEM) images to compute elastic constants from small rock fragments. This method can also be applied to data from whole core computed tomography (CT) scans. This approach was used to develop a digital rock workflow to compute elastic properties from rotary sidewalls cores, drill cuttings, and core CT data.
The new approach combines quantitative information obtained from 2D ion-milled SEM images with rock physics effective-medium models, the latter used to relate volume properties to elastic properties. These models can be obtained from wireline and/or laboratory measurements of bulk rock volumetrics together with elastic rock properties. This process of finding a rock physics model is called rock physics diagnostics.
The SEM images provide porosity, organic matter volume, and pore structure. The mineralogy of the sample obtained through quantitative X-ray diffraction (XRD) is added to those inputs. Well log data relevant to the local area are then used to establish a rock physics model linking the elastic properties to porosity, organic matter content, and mineralogy. These models are established for each basin and formation, based on available wireline log data. High quality wireline data is key to successful rock physics diagnostics (RPD).
In this study, wireline logs and core samples were obtained from a well in Culberson Co, TX. The zone of interest in this case was the Wolfcamp A formation. After establishing the appropriate rock physics effective medium models, the elastic properties were computed, including Young's modulus, Poisson's ratio, compressional wave velocity, and shear wave velocity from SEM images and XRD mineral data. The computed, upscaled elastic properties closely matched the log variability.
This method can be used to obtain the required elastic properties from wells that lack compressional and dipole shear wave data. This mechanical properties data can be used to compute horizontal stress, unconfined compressive strength, and other critical properties that control hydraulic fracture growth. In many cases, drill cuttings can be used for the SEM analysis. This new approach does not require cores, and so can be especially valuable in quantifying elastic and mechanical properties along the lateral wellbore where wireline logs are seldom available.
Rock mechanical properties is essential for several geomechanical applications such as wellbore stability analysis, hydraulic fracturing design, and sand production management. These are often reliably determined from laboratory tests by using cores extracted from wells under simulated reservoir conditions. Unfortunately, most wells have limited core data. On the other hand, wells typically have log data, which can be used to extend the knowledge of core-based mechanical properties to the entire field. Core to log integration of rock mechanical properties and its interpretation is limited by our current understanding of rock physics. The gap is clearly evident where approximations such as empirical relationship between dynamic and static mechanical properties are used for rock failure estimation. This paper presents a hybrid framework that combines advances in digital rock physics (DRP) and machine learning (ML) to predict rock mechanical propertiy (e.g., Young's modulus) from rock mineralogy and texture to improve the accuracy of mechanical properties determined from log data.
In this study, mineralogy, density, and porosity data are obtained from routine core analysis and rock mechanical property from triaxial compression tests. In our methodology, we utilized DRP models which were calibrated against core data and then generate rock mechanical property, for intervals for which triaxial measurements were not available. Mineralogy and texture data are used to create DRP models by numerically simulating rock-forming geological process including sedimentation, compaction, and cementation. Rock mechanical properties derived from DRP are used to enhance the set of training data for the ML algorithm to establish a correlation between rock mineralogy, texture, and mechanical property and construct the ML-based rock mechanical property model. The ML model generates Young's modulus predictions and are compared with the laboratory measurements.
The predicted Young's modulus of rock models from the combined approach has a good agreement with the laboratory measurements. Two quantitative measures for estimation accuracy are calculated and examined including the correlation coefficient and the mean absolute percentage error. Cross-correlation plots between the Young's modulus predicted from the ML model and experimental results show high correlation coefficients and small error. The results of the study show that DRP model can be used to feed the ML model with reliable data so that the prediction accuracy can be improved. The results of this work will provide an avenue of learning from the formation lithology and using the knowledge to predict rock mechanical properties.
Basin simulations, reservoir simulations, laboratory measurements and field measurements are crucial details needed for making good operational decisions in frontier areas. Seismic reservoir characterization is the task that combines engineering, geological and geophysical data. Basin simulation gives the geoscientist the opportunity to incorporate sophisticated modeling into their predictions of subsurface properties. This simulation technique normally uses a regional seismic interpretation as an endpoint for a compaction, temperature, pressure or mineralogical forward model that has engineering and geophysical calibrations. Reservoir characterization work often produces multiple interpretations, using various techniques, of the same volume of the earth. How should these interpretations be combined? Which interpretations should carry more influence?
The technological challenge of using basin simulation output with traditional seismic inversion is that the exact location of facies is not accurate. Therefore, the derived static low frequency model constructed using rock physics transforms leads to an inversion product with unphysical artifacts at worst and at best, a reiteration of the basin model with slight property variations from the seismic amplitude input conspicuously overlying.
We present an inversion that utilizes a Bayesian framework to iteratively constructs a facies and impedance model using prior estimates of facies distribution and impedance uncertainty. This framework allows the spatial variability of properties from the basin model to be included in the inversion without introducing localized artifacts. The benefit of using a Bayesian framework in deterministic inversion at seismic resolution is that priors may be considered in order to disqualify unphysical or unlikely yet acceptable solutions from the non-unique solution space. In this application, the prior is constructed using facies specific porosity compaction trends, cement profiles based on temperature and timing and pore pressures, transformed with rock physics models to elastic properties. With these facies property volumes, we produce unique probability density functions at every seismic sample. Given the seismic input and additional priors, the inversion produces a most probable facies volume and impedances (Vp-Vs-Density). The resulting properties are thus an integration of a complex basin simulation model with a deterministic seismic inversion.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
In this paper we propose a new rock physics workflow which uses a combination of the Hashin-Shtrikman bounds (Hashin and Shtrikman, 1962) and the Joint Self Consistent Approximation (Bruggeman, 1935; Landauer, 1952; Berryman, 1995) and Differential Effective Medium model (Bruggeman, 1935; Sen
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 202A (Anaheim Convention Center)
Presentation Type: Oral