Prediction Of Reservoir Properties Research Articles

Improved application of flow unit method to predict reservoir properties in uncored wells

Improved application of flow unit method to predict reservoir properties in uncored wells

Outcrop analogue study to determine reservoir properties of the Los Humeros and Acoculco geothermal fields, Mexico

Abstract. The Los Humeros geothermal system is steam dominated and currently under exploration with 65 wells (23 producing). Having temperatures above 380 ∘C, the system is characterized as a super hot geothermal system (SHGS). The development of such systems is still challenging due to the high temperatures and aggressive reservoir fluids which lead to corrosion and scaling problems. The geothermal system in Acoculco (Puebla, Mexico; so far only explored via two exploration wells) is characterized by temperatures of approximately 300 ∘C at a depth of about 2 km. In both wells no geothermal fluids were found, even though a well-developed fracture network exists. Therefore, it is planned to develop an enhanced geothermal system (EGS). For better reservoir understanding and prospective modeling, extensive geological, geochemical, geophysical and technical investigations are performed within the scope of the GEMex project. Outcrop analogue studies have been carried out in order to identify the main fracture pattern, geometry and distribution of geological units in the area and to characterize all key units from the basement to the cap rock regarding petro- and thermo-physical rock properties and mineralogy. Ongoing investigations aim to identify geological and structural heterogeneities on different scales to enable a more reliable prediction of reservoir properties. Beside geological investigations, physical properties of the reservoir fluids are determined to improve the understanding of the hydrochemical processes in the reservoir and the fluid-rock interactions, which affect the reservoir rock properties.

Rock Physics Modeling Assisted Reservoir Properties Prediction: Case Study in Malay Basin

Shear velocity log is not measured at all wells in oil and gas fields, thus rock physics modeling plays an important role to predict this type of log. Therefore, seismic pre stack inversion is performed and elastic properties are estimated more accurately. Subsequently, a robust Petro-Elastic relationship arising from rock physics model leads to far more precise prediction of petrophysical properties. The more accurate rock physics modeling results in less uncertainty of reservoir modeling. Therefore, a valid rock physics model is intended to be built. For a better understanding of reservoir properties prediction, first of all rock physics modeling for each identified litho-facies classes should be performed separately through well log analysis.

Petroleum reservoir quality prediction: overview and contrasting approaches from sandstone and carbonate communities

Abstract The porosity and permeability of sandstone and carbonate reservoirs (known as reservoir quality) are essential inputs for successful oil and gas resource exploration and exploitation. This chapter introduces basic concepts, analytical and modelling techniques and some of the key controversies to be discussed in 20 research papers that were initially presented at a Geological Society conference in 2014 titled ‘Reservoir Quality of Clastic and Carbonate Rocks: Analysis, Modelling and Prediction’. Reservoir quality in both sandstones and carbonates is studied using a wide range of techniques: log analysis and petrophysical core analysis, core description, routine petrographic tools and, ideally, less routine techniques such as stable isotope analysis, fluid inclusion analysis and other geochemical approaches. Sandstone and carbonate reservoirs both benefit from the study of modern analogues to constrain the primary character of sediment before they become a hydrocarbon reservoir. Prediction of sandstone and carbonate reservoir properties also benefits from running constrained experiments to simulate diagenetic processes during burial, compaction and heating. There are many common controls on sandstone and carbonate reservoir quality, including environment of deposition, rate of deposition and rate and magnitude of sea-level change, and many eogenetic processes. Compactional and mesogenetic processes tend to affect sandstone and carbonate somewhat differently but are both influenced by rate of burial, and the thermal and pressure history of a basin. Key differences in sandstone and carbonate reservoir quality include the specific influence of stratigraphic age on seawater composition (calcite v. aragonite oceans), the greater role of compaction in sandstones and the greater reactivity and geochemical openness of carbonate systems. Some of the key controversies in sandstone and carbonate reservoir quality focus on the role of petroleum emplacement on diagenesis and porosity loss, the role of effective stress in chemical compaction (pressure solution) and the degree of geochemical openness of reservoirs during diagenesis and cementation. This collection of papers contains case study-based examples of sandstone and carbonate reservoir quality prediction as well as modern analogue, outcrop analogue, modelling and advanced analytical approaches.

Седиментационное моделирование как основа прогноза коллекторских свойств органогенных построек месторождений им. Р. Требса и им. А. Титова

Седиментационное моделирование как основа прогноза коллекторских свойств органогенных построек месторождений им. Р. Требса и им. А. Титова

Joint probabilistic petrophysics-seismic inversion based on Gaussian mixture and Markov chain prior models

Seismic reservoir characterization focuses on the prediction of reservoir properties based on the available geophysical and petrophysical data. The inverse problem generally includes continuous properties, such as petrophysical and elastic attributes, and discrete properties, such as lithology/fluid classes. We have developed a joint probabilistic inversion methodology for the prediction of petrophysical and elastic properties and lithology/fluid classes that combined statistical rock physics and Bayesian seismic inversion. The elastic attributes depend on continuous petrophysical variables, such as porosity and clay content, and discrete lithology/fluid classes, through a nonlinear rock-physics relationship together. The seismic model relates the elastic attributes, such as velocities and density, to their seismic response (reflectivity, traveltime, and amplitudes). The advantage of our integrated approach is that the inversion method accounts for the uncertainty associated to each step of the modeling workflow. The lithology/fluid classes are assigned by a Markov random field prior model to capture vertical continuity and vertical sorting of the lithology/fluid classes. Because rock and fluid properties are in general not Gaussian, a spatially coupled Gaussian mixture prior model based on the lithology/fluid classes is constructed. The forward geophysical operator includes a lithology-/fluid-dependent rock physics model and a linearized seismic model based on the convolution of the seismic wavelet with the reflectivity coefficient series. The solution of the inverse problem consists of the posterior distributions of petrophysical and elastic properties and lithology/fluid classes. We proposed an efficient Markov chain Monte Carlo algorithm to sample from the posterior models and assess the uncertainty. Our methodology is demonstrated on a seismic cross section from a survey in the Norwegian Sea, and it shows promising results consistent with well-log data measured at the well location as well as reliable prediction uncertainties.

Modern geodynamics and its influence on the secondary filtration properties of the collectors of the northeast part of the Sakhalin shelf

The complex tectonic structure of the Okhotsk Sea shelf and high geodynamic activity makes it necessary to use of geomechanical modeling for the prediction of the secondary reservoir properties of collectors. 3D model of fractured porosity and permeability for Kirinskoye, Ayashsky and East-Odoptinsky license areas was built. The results of modeling revealed filtration channels where can occur both vertical and horizontal migration of hydrocarbons. It is found that the secondary permeability significantly is different of the upper and lower stratigraphic horizons and the okobykayskaya clay stratum is the surface, which is a regional tire. All calculations were performed on a 3D grid geological model, which allows using the data for hydrodynamic modeling.

Early diagenesis driven by widespread meteoric infiltration of a Central European carbonate ramp: A reinterpretation of the Upper Muschelkalk

Meteoric diagenesis of carbonate ramps is often difficult to interpret and can commonly be confused with other coinciding diagenetic processes. The Middle Triassic Upper Muschelkalk of Switzerland provides an insightful case in which the effects of several overprinting diagenetic environments, including matrix dolomitization, can be clearly unravelled. Previous studies suggested that diagenesis took place in connate marine waters, with later meteoric waters being invoked to explain recrystallization of dolomite. In this study, diagenetic analyses (C–O stable isotope ratios, thin-section point counting, cathodoluminescence and UV-fluorescence microscopy) of calcitic bioclastic samples have revealed that early diagenesis (pre-stylolitization) and the accompanying porosity evolution did not occur exclusively in the presence of marine fluids. Five sequential stages of diagenesis have been identified: marine, shallow burial, mixing-zone, meteoric and dolomitization. Marine diagenesis induced precipitation of bladed and inclusion-rich syntaxial cements that fluoresce strongly under UV-light. Both cements account for a mean 7.5vol% reduction in the porosity of bioclastic beds. Shallow burial diagenesis likely induced mouldic porosity and associated fluorescent dog-tooth cementation. Based on light oxygen isotope and elevated strontium isotope ratios, matrix aragonite–calcite neomorphism is interpreted to have occurred in a mixture of marine and meteoric fluids. The combination of shallow burial and mixing-zone processes reduced porosity on average by 4.8vol%. Evidence for subsequent meteoric diagenesis is found in abundant dog-tooth and blocky calcite cements that have mean δ18OVPDB of −9.36‰ and no signs of recrystallization. These meteoric cements reduced porosity by a further 13.4vol%. Percolation of meteoric water through the ramp was driven by hydraulic gradients on an adjacent basement high, which was exposed by a cycle of early Ladinian regressions. Following meteoric diagenesis the Upper Muschelkalk was dolomitized by refluxing brines. This complex history of diagenesis resulted in moderate porosities in dolomitized rocks (up to 20%), and low porosities (<5%) in calcitic bioclastic beds. These results are used to show that the present-day reservoir properties of non-dolomitized carbonate rocks, particularly bioclastic beds, can be largely attributed to early diagenetic processes. Thus, knowledge of the early diagenetic history and its regional controls provides a means to predict reservoir properties over wide areas between and beyond well sites.

Structural heritage, reactivation and distribution of fault and fracture network in a rifting context: Case study of the western shoulder of the Upper Rhine Graben

In fractured reservoirs in the basement of extensional basins, fault and fracture parameters like density, spacing and length distribution are key properties for modelling and prediction of reservoir properties and fluids flow. As only large faults are detectable using basin-scale geophysical investigations, these fine-scale parameters need to be inferred from faults and fractures in analogous rocks at the outcrop. In this study, we use the western shoulder of the Upper Rhine Graben as an outcropping analogue of several deep borehole projects in the basement of the graben. Geological regional data, DTM (Digital Terrain Model) mapping and outcrop studies with scanlines are used to determine the spatial arrangement of the faults from the regional to the reservoir scale. The data shows that: 1) The fault network can be hierarchized in three different orders of scale and structural blocks with a characteristic structuration. This is consistent with other basement rocks studies in other rifting system allowing the extrapolation of the important parameters for modelling. 2) In the structural blocks, the fracture network linked to the faults is linked to the interplay between rock facies variation linked to the rock emplacement and the rifting event.

Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

The effect of full-azimuth local angle domain (LAD) imaging on the study of terrigenous and carbonate reservoirs under complex in-situ conditions at an eastern Siberian field

The EarthStudy 360 full-azimuth local angle domain (LAD) imaging and analysis technology, developed by Paradigm, appeared on the geophysical services market in 2012. GazpromNeft NTC, a high-tech company that strives to incorporate the most advanced systems into its operations, did not let this opportunity pass by. A pilot project to test LAD imaging was undertaken in 2014. The study’s most significant findings were anisotropy intensity and direction determinations in terrigenous and carbonate reservoirs, and improved acoustic impedance convergence calculated on a combination of advanced technology results and GIS data. In 2015 GazpromNeft NTC used the technology to assess 3D full-azimuth angle domain survey data in an Eastern Siberian field. Historically, Eastern Siberia has been one of the most difficult areas for seismic studies attempting to prospect for and predict reservoir properties. Complex relief and subsurface velocity heterogeneity in both vertical and lateral directions creates problems for seismic survey operations and for seismic data processing. The greatest challenges occur in depth processing, where specific complexities hinder the development of a depth/velocity model, beginning with near-surface formations and the top portion of the geologic profile. Local lithological variations in the lateral direction, accompanied by the surface exposure of rocks of various ages and lithological characteristics (from carbonates to clays, salt-bearing strata, and sometimes trapped intrusions) make it necessary to develop complex velocity models and apply full-azimuth depth surveys which traditional approaches and migrations, based on the Kirchhoff integral, cannot provide. Cambrian, Jurassic and Quaternary rocks with P-wave velocities of 5000-5500 m/s, 3000-3500 m/s, and 900-1200 m/s, respectively, are exposed at the surface of the survey area.

A non-equilibrium molecular dynamics study of methane transport in clay nano-pores

Accurate characterization of shale reservoir properties is vitally important for reliable gas production forecasting and reserve estimation. In this work, we use boundary-driven non-equilibrium molecular dynamics (BD-NEMD) to investigate methane transport in porous clays, which are one of the major mineral components of shale. One of the critical issues in BD-NEMD simulations is robust temperature control schemes to maintain isothermal conditions when the external force is applied to drive fluid flow. To this end, we scrutinize the performance of six temperature control schemes for BD-NEMD. We identify a number of effective temperature control schemes, and we show that careful algorithm selection can considerably reduce the number of simulations required to estimate transport diffusion coefficients. Using a robust temperature control scheme identified in our study, we examine the validity of the Knudsen model for predicting methane diffusivities in clay nano-pores. Although the Knudsen model is one of the most widely used theoretical approaches for predicting reservoir properties, we find that it fails to accurately predict methane transport diffusivities in small clay nano-pores. Our findings therefore suggest that reservoir models based on Knudsen theory may result in significant over- or under-prediction of gas production, depending on reservoir conditions. These findings are consistent with other studies, which posit that the Knudsen diffusion model breaks down in nano-confined systems because it neglects to account for the effects of adsorption on gas transport.

Integration of seismic and well-log data using statistical and neural network methods

In the last two to three decades, the use of seismic attributes for reservoir characterization and modeling has grown exponentially. Now, a dozen or more attributes are often extracted from seismic data to predict reservoir properties. Meanwhile, an increasing trend of acquiring more wireline logs provides more and more data to describe reservoir properties. Both statistical methods and artificial neural networks (ANNs) are often used to extract information and make predictions. Although statistical methods and ANNs provide powerful tools for geoscience data integration, they also have pitfalls. We present principal component analysis (PCA) and ANNs for facies classifications and porosity prediction. We also show the use and limitations of these methods and the importance of integrating the geologic and petrophysical knowledge.

Reservoir properties prediction using extended elastic impedance: the case of Nianga field of West African Congo basin

Simultaneous seismic inversion and extended elastic impedance (EEI) were applied to obtain quantitative estimates of porosity, water saturation, and shale volume over Nianga field of Congo basin, West Africa. The optimum angle at which EEI log and the target petrophysical parameter give the maximum correlation was meticulously analyzed by additionally incorporating the concept of relative rock physics. Prestack seismic data were simultaneously inverted into Vp, acoustic, and gradient impedances. The last two broadband inverted volumes were projected to Chi angles corresponding to the target petrophysical parameters, and three broadband EEI volumes were obtained. At well control points, the linear trends based on specific lithology between EEI and petrophysical parameters were then used to transform EEI volumes into quantitative porosity, water saturation, and shale volume cubes. In order to obtain the reservoir facies distribution, another concept of minimum energy angle was used to generate the background EEI cube, thereby enabling the mapping of reservoir facies. From quantitative porosity, water saturation, shale content, and background EEI cubes, favorable zones have been pinpointed which may suggest possible drilling locations for future development of the field.

Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization

The successful applications of the conventional Computational Intelligence (CI) techniques and Hybrid Intelligent Systems (HIS) in petroleum reservoir characterization have been reported. However, these techniques are limited in their capability to handle a single hypothesis of a problem at a time. The major objective of the reservoir characterization process is to produce models that are robust enough to help improve the accuracy of the predictions of reservoir properties for use in full-field and large-scale simulation models. Research in CI continues to evolve new techniques and paradigms to meet this noble objective. It has been shown that there are uncertainties in the reservoir characterization process as well as the optimal choice of CI/HIS models parameters. The main challenge is to develop models that are capable of handling multiple hypotheses to reduce the uncertainties thereby ensuring optimal solutions. The ensemble machine learning paradigm has been established to tackle this challenge. This new machine learning technology has not been adequately explored in handling some of the petroleum engineering challenges. This paper rigorously reviews the concept of ensemble learning paradigm, presents successful applications outside petroleum engineering and the geosciences, discusses a few successful attempts in petroleum engineering and the geosciences, and concludes with some recommendations for the much-needed future applications.

Complex resistivity spectra and pore geometry for predictions of reservoir properties in carbonate rocks

Measurements of complex resistivity spectra (CRS) are performed on core plugs from a wide variety of carbonates in a log sweep from 0.1 to 100,000Hz inside a pressurized chamber at varying effective pressures up to 20MPa. We quantify the CRS curves by extracting the slope of both real and imaginary part of complex resistivity in the 10,000–100,000Hz range. Pore geometries are quantified with thin-section digital image analysis (DIA) from optical light microscopy. The dataset includes 330 carbonate core plug samples from twelve different study areas and hence includes a highly diverse range of carbonate rock types. This should make our results applicable to most carbonate rocks.Pore geometry parameters derived from DIA, such as Dominant Pore Size (DOMsize) and Perimeter over Area (PoA), correlate well with petrophysical properties such as cementation factor and permeability. However, when modeling those properties, higher correlation coefficients are achieved with CRS than with DIA parameters. Using CRS and model constants tuned to the sub-datasets, cementation exponents are predicted with R2=0.91 and permeability with R2=0.84. The correlation coefficient of a universal equation for all 330 samples is still high for cementation factors with R2=0.80, but less for permeability with R2=0.48.The results show that CRS in carbonates are directly related to permeability and formation factors, and greatly improve reservoir property estimates. This study also highlights the usability of low-frequency CRS data as a measure of flow and storage properties in carbonate rocks. The transfer of this methodology to wireline applications would result in more accurate and continuous permeability and cementation factor predictions from well logs.

Quantitative interpretation using inverse rock-physics modeling on AVO data

Quantitative seismic interpretation has become an important and critical technology for improved hydrocarbon exploration and production. However, this is typically a resource-demanding process that requires information from several well logs, building a representative velocity model, and, of course, high-quality seismic data. Therefore, it is very challenging to perform in an exploration or appraisal phase with limited well control. Conventional seismic interpretation and qualitative analysis of amplitude variations with offset (AVO) are more common tools in these phases. Here, we demonstrate a method for predicting quantitative reservoir properties and facies using AVO data and a rock-physics model calibrated with well-log data. This is achieved using a probabilistic inversion method that combines stochastic inversion with Bayes' theorem. The method honors the nonuniqueness of the problem and calculates probabilities for the various solutions. To evaluate the performance of the method and the quality of the results, we compare them with similar reservoir property predictions obtained using the same method on seismic-inversion data. Even though both approaches use the same method, the input data have some fundamental differences, and some of the modeling assumptions are not the same. Considering these differences, the two approaches produce comparable predictions. This opens up the possibility to perform quantitative interpretation in earlier phases than what is common today, and it might provide the analyst with better control of the various assumptions that are introduced in the work process.

Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA

Collocated cokriging (CCK) and neural-network multi-attribute transform (NN-MAT) are widely used in the prediction of reservoir properties because they can integrate sparsely-distributed, high-resolution well-log data and densely-sampled, low-resolution seismic data. CCK is a linear-weighted averaging method based on spatial covariance model. NN-MAT, based on a nonlinear relationship between seismic attributes and log values, treats data as spatially independent observations. In this study, we analyzed 3-D seismic and well-log data from the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA to investigate: (1) how CCK and NN-MAT perform in the prediction of porosity and (2) how the number of wells affects the results. Among a total of 64 wells, 25 wells were selected for CCK and NN-MAT and 39 wells were withheld for validation. We examined four cases: 25, 20, 15, and 10 wells. CCK overpredicted the porosity in the validation wells for all cases likely due to the strong influence of high values, but failed to predict very large porosities. Overprediction of CCK porosity becomes more pronounced with decreasing number of wells. NN-MAT largely underpredicted the porosity for all cases probably due to the band-limited nature of seismic data. The performance of CCK appears to be not affected significantly by the number of wells. Overall, NN-MAT performed better than CCK although its performance decreases continuously with decreasing number of wells.

Tight gas development in the Mezardere Formation, Thrace Basin Turkey

More than 600 m of Lower Oligocene and Upper Eocene marine sedimentary rocks contain tight gas resources in the Thrace Basin of Turkey. Positive hydraulic fracture stimulation results, along with the presence of many untested, off-structure stratigraphic traps, indicate there is a large potential for future tight gas development in the basin.Extracting value from these complex set of reservoirs needs the use of state of the art technologies. Many companies have recognized that this theme requires the Asset Team project management methodology wherein geologists, geophysicists, engineers, log analysts, and other professionals come together to combine their skills and insight to better understand, measure and predict reservoir properties in low-permeability reservoirs and to use that information in resource evaluation, reservoir characterization and management.This manuscript describes how such a multidisciplinary asset team approach was used to determine the petrography, reservoir and petrophysical properties of the tight gas reservoirs in the Thrace Basin Turkey. Geological details and detailed core studies were integrated into petrophysical analyses, along with mud logs to identify the unconventional pay zones for executing hydraulic fracture stimulation in the area. As a result, stimulations were designed to avoid frequent interlaced water-bearing sand stringers and resulted in numerous successful recompletions of the existing wells, drilling of vertical and horizontal wells with increasing recoverable reserve by over 40 Billion Standard Cubic Feet (BCF) in proved reserves during a 2 year project.

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Various petroleum reservoir properties have been estimated in literature using only one of log, seismic or production data. The recent trend in data mining is integrating multi-modal and multi-dimensional data for improved reservoir properties prediction. The objective of this paper is to employ hybrid machine learning and feature-selection based predictive models to estimate the permeability of carbonate reservoirs from integrated seismic and well log data. Hybrid models of Type-2 Fuzzy Logic System (T2FLS) and Support Vector Machine (SVM) with Functional Networks (FN) as the non-linear feature selection algorithm are proposed. Five seismic attributes were integrated with six commonly used Well logs. Data were collected from 33 oil Wells but only 17 of them had a complete matching seismic-log pair. The performance of the hybrid models were compared to those of the individual models without the non-linear but with the conventional feature selection algorithm. The comparative results showed improved prediction accuracies with the hybrid models and more excellently with the FN-SVM model. A blind test on the models revealed that the FN-SVM hybrid model gave an (R-Square) of 0.82, root mean square error of 0.46, and mean absolute error of 0.42 compared to the lowest performing T2FLS model with 0.40, 0.77 and 0.65 respectively. This demonstrates the significance of the hybrid machine learning paradigm in solving petroleum engineering problems with improved accuracies. The study presented some lessons learned from the data limitation challenges experienced in this work and proposed recommendations to chart further research directions.