Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Fluid Characterization
Abstract Significant advances have been made in formation testing since the introduction of wireline pumpout testers (WLPT), particularly with respect to downhole fluid compositional measurements. Optical sensors and the use of spectroscopic methods have been developed to improve sample quality and minimize sampling time in downhole environments. As a laboratory technique, spectroscopy is a ubiquitous and powerful technology that has been used worldwide for decades to measure the physical and chemical properties of many materials, including petroleum, geological, and hydrological samples. However, laboratory-grade, high-resolution spectrometers are incompatible with the hostile environments encountered downhole, at wellheads, and on pipelines. Only limited resolution techniques are available for the rugged conditions of the oil field. This paper introduces a new optical technology that can provide high-resolution, laboratory-quality analyses in harsh oilfield environments. A new technology for optical sensing, multivariate optical computing (MOC), has been developed and is a non-spectroscopic technique. This new sensing method uses an integrated computation element (ICE) to combine the power and accuracy of high-resolution, laboratory-quality spectrometers with the ruggedness and simplicity of photometers. Many modern sensors typically merge the sensor with the electronics on an integrated computing chip to perform complex computations, resulting in an elegant yet simplistic design. Now, optical sensing using ICE features an analogue optical computation device to provide a direct, simple, and powerful mathematical computation on the optical information, completely within the optical domain. Because the entire optical range of interest is used without dispersing the light spectrum, the measurements are obtained instantly and rival laboratory-quality results. A proof of concept MOC with ICE has been demonstrated, logging more than 7,000 hours, in nearly continuous use for 14 months. Oils with gravities ranging from 14 to 65°API have been measured in downhole environments that range from 3,000 to 20,000 psi, and from 150 to 350°F. Hydrocarbon composition measurements, including saturates, aromatics, resins, asphaltenes, methane, and ethane, have been demonstrated using the MOC configuration. As compositional calculations therein, GOR and density are validated to within 14 scf/bbl and 1%, respectively. The paper discusses the details of the new ICE-based sensor and describes its adaptations to downhole applications.
- Research Report (0.67)
- Overview > Innovation (0.34)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.68)
Abstract This paper describes a workflow that was applied to a carbonate field in Oman to derive fracture and effective permeability models that were validated with multiple blind wells and reservoir simulation. The studied block is the largest and most faulted within a field which is currently under water-flood FDP. The study was kicked off with extensive borehole image interpretation. In parallel, several high resolution seismic inversions and spectral imaging attributes were generated as drivers to geological and fracture modelling. High resolution seismic was used to highlight subtle faults. Facies changes were also visible from seismic as seen in cored wells. Sequential geological modelling of GR, density, porosity and SW was carried out and constrained by seismic attributes. The derived fracture frequency logs were compared against geological, structural and seismic drivers in a process called driver ranking. The results confirmed the role of faults as well as facies being primary controls of fracturing. Subsequently, the screened and cross-correlated potential drivers were carried forward to constrain the fracture models. Multiple stochastic realizations were derived through neural network training and testing and an average model was kept. Final models were validated against hidden BHI data. A new BHI was used to confirm model prediction. Different types of dynamic data in non-BHI wells were also used to validate the fracture models as specific production/injection related issues could be directly linked to presence of fractures. These data include PLT, PTA and tracer tests from which injectivity issues and short circuiting were explained by higher fracture densities and corridors derived from modeling. Through dynamic calibration, the fracture model was converted to fracture permeability. The fracture permeability is the product of fracture density and a scaling factor derived from history matching. Subsequently, the addition of matrix permeability and fracture permeability will determine the effective permeability. This Keffective was directly used in the reservoir simulator without upscaling since it was part of the same grid hosting the fracture models. The results were encouraging as the simulation was smooth and error-free.
- Geophysics > Seismic Surveying > Seismic Interpretation (0.37)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.34)
- Geophysics > Seismic Surveying > Seismic Modeling (0.34)
Challenges and Key Learning for Developing Tight Carbonate Reservoirs
George, Bovan K (Abu Dhabi Company for Onshore Oil Operations (ADCO)) | Clara, Cedric (Abu Dhabi Company for Onshore Oil Operations (ADCO)) | Al Mazrooei, Suhaila (Abu Dhabi Company for Onshore Oil Operations (ADCO)) | Manseur, Saadi (Abu Dhabi Company for Onshore Oil Operations (ADCO)) | Abdou, Medhat (Abu Dhabi Company for Onshore Oil Operations (ADCO)) | Chong, Tee Sin (Abu Dhabi Company for Onshore Oil Operations (ADCO)) | Al Raeesi, Muna (Abu Dhabi Company for Onshore Oil Operations (ADCO))
Abstract Fast track development projects, with timely data acquisition plans for development optimization, are very challenging for tight and heterogeneous carbonate reservoirs. This paper presents the challenges and key learning from initial stages of reservoir development with limited available data. Focus of this study is several stacked carbonate reservoirs in a giant field located in onshore Abu Dhabi. These undeveloped lower cretaceous reservoirs consist of porous sediments inter-bedded with dense layers deposited in a near shore lagoonal environment. The average permeability of these reservoirs is in the range of 0.5-5 md. Mapping the static properties of these reservoirs is difficult since they are not resolved on seismic due to the low acoustic impedance contrast with adjacent dense layers. Petrophysical evaluation of thin porous bodies inter-bedded with dense layers in highly deviated wells pose significant challenges. Laterolog type LWD resistivity measurements which are less affected by environmental effects, offer more accurate formation resistivity compared to propagation type measurements. With limited suite of logs, some of the zones with complex lithology had to be evaluated innovatively as detailed in the paper. Integrated studies are initiated to improve reservoir description by carrying out accurate permeability mapping, SCAL, geomechanical and diagenesis & rock typing studies. Significant challenges exist regarding the development of thin, tight and highly heterogeneous reservoirs, in terms of recovery mechanism, well architecture, well count, drilling, well completion and economics. Static and dynamic models were used extensively to evaluate different development scenarios and conduct sensitivity studies to bracket uncertainties. Various geo-steering options were discussed and the paper also details maximizing the reservoir productivity using long reach MRC (Maximum Reservoir Contact) wells. Tight and heterogeneous reservoirs call for extensive and real time reservoir surveillance activities to assess well performance and reservoir connectivity. This paper highlights how these challenges are overcome through upfront surveillance planning and proactive well completion strategy.
- Geology > Geological Subdiscipline > Geomechanics (0.88)
- Geology > Sedimentary Geology > Depositional Environment > Transitional Environment > Lagoonal Environment (0.54)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.47)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government (0.46)
Abstract Geochemical fingerprinting using gas chromatography techniques is a proven alternative or additional tool to traditional approaches for the production back-allocation such as metering or production logging tools. It can be applied in various scenarios, from commingled reservoirs in a single well to allocation of multiple wells or entire fields produced via the same evacuation system. The approach is fast, cost-effective and does not require interruption of production, thus enabling frequent monitoring of production. The method is based on detailed comparison of fluid compositions obtained from gas chromatography of representative samples acquired from the point of interest (single reservoir, well, etc.), called further the ‘end-member’ and the ‘commingled fluid’ to be allocated. Production allocation using a geochemical fingerprinting approach has been successfully used across the globe with specific traction in North America, the North Sea region and the Middle East. Our method is based on analysis of ratios of heights of neighboring chromatographic peaks (compounds) rather than the single peak heights or areas that all the chromatograms have in common. Such approach reduces inconsistencies between light and heavy hydrocarbons due to some problems of reproducibility during the sampling or during the analysis. It also allows us to tackle issues related to the changes in compositions of end-members during production. In addition, the resolution manages the non-linearity of the equations derived from the physics of the mixtures. The non-Gaussian distribution of the errors is taken into account to comply with the maximum likelihood. Thus, a solid theoretical framework is established to avoid current issues encountered when peak ratios are utilized. Benefits of this method include firstly, a complete management of the uncertainties on the proportions of end-members and on each individual peak ratio employed. In addition to minimization of ‘calibration’ lab mixtures, elimination of manual peak selection (sometimes subjective). Finally, with this methodology employed heir in there is theoretically, no limitation on the number of end-members. In this paper we demonstrate our approach applied successfully on a series of case studies including biodegraded oils and ‘annoyingly’ similar fluids. We demonstrate that our approach can be successfully and cost-effectively applied to allow for more reliable reservoir/field management.
- Asia > Middle East (0.48)
- North America > United States (0.46)
- Europe > United Kingdom > North Sea (0.24)
- (3 more...)
- Reservoir Description and Dynamics > Fluid Characterization > Geochemical characterization (1.00)
- Production and Well Operations (1.00)
Abstract Productivity in deep-basin tight gas reservoirs can be improved significantly by natural fracture enhanced permeability. Therefore, deviated and horizontal wells are often drilled to intersect highly fractured formations. Unfortunately, fractured reservoirs are highly heterogeneous, often characterized by probability distributions of fracture properties in a discrete fracture network (DFN) model. In addition, the relationship between recovery response and model parameters is vastly non-linear, rendering the process of conditioning reservoir models to both static and dynamic (production) data challenging. In the current paper, a novel approach is presented for uncertainty assessment and characterization of fractured reservoir model parameters using data from diverse sources. First, Monte Carlo based techniques were used to generate multiple DFN models conditioned to geological and tectonic information, accounting for the uncertainty associated with static data. Next, each model or realization was upscaled for flow simulation. Finally, Ensemble Kalman Filter (EnKF), a data assimilation technique that has been used for assisted history matching, was employed to update the DFN models using production data. In order to ensure positive definiteness of the updated permeability tensors, to reduce the size of model parameter space, and to eliminate the redundancy between parameters for improved convergence, principal component analysis was performed such that only the main principal components of the full permeability tensor and sigma factors were updated through EnKF algorithm. The qualities of the history-matched models were assessed by comparing the spatial distribution of the updated model parameters with the initial ensemble, as well as the Root Mean Square Error (RMSE) of the predicted data mismatch. The results clearly demonstrate that, characterization of fractured reservoirs combining DFN modeling with updating principal components of the upscaled model parameters through EnKF has the potential to resolve the shortfall of traditional techniques for history matching of such complicated reservoirs. The proposed approach can be used effectively to update reservoir models and optimize development plans in unconventional gas reservoirs using continuous flow and pressure measurements.
- North America > United States (1.00)
- North America > Canada > Alberta (0.89)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin (0.99)
Abstract Reliable design of solvent injection for enhanced heavy-oil recovery requires accurate representation of multiphase behavior for heavy-oil/solvent mixtures in a wide range of pressure-temperature-composition conditions. Characterization of a heavy oil is more difficult than that of a conventional oil because the former is conducted under more uncertainties in composition and PVT data. Volume-shift parameters are often required to improve density predictions, separately from compositional behavior predictions, in conventional fluid characterization methods (CM). Thermodynamically, however, volumetric behavior predictions (e.g., densities) are consequences of compositional behavior predictions. In this paper, we develop a new fluid characterization method (NM) that gives accurate multiphase behavior representation for heavy-oil/solvent mixtures without using volume-shift parameters. The Peng-Robinson (PR) EOS is used with the van der Waals mixing rules. In the NM, pseudo components are initially assigned critical temperature (TC), critical pressure (PC), and acentric factor (ω) values that are optimized for the PR EOS for accurate phase behavior predictions for n-alkanes from C7 to C100. The subsequent regression process searches for an optimum set of TC, PC, and ω in physically justified directions. The regression algorithm developed does not require user's experience of thermodynamic modeling for robust convergence. The NM also satisfies Pitzer's definition of ω for each component. The NM is compared with the CM in terms of various types of phase diagrams, minimum miscibility pressure calculations, and 1-D oil displacement simulations. Twenty two different reservoir oils are used in the comparisons. Results show that the NM with 11 components gives phase behavior predictions that are nearly identical to those using the CM with 30 components. A 1-D simulation case study presents that the NM can robustly reduce dimensionality of composition space while keeping accurate multiphase behavior predictions along composition paths at different dispersion levels tested. We show that the CM with volume shift can give erroneous phase behavior and oil recovery predictions in compositional simulation. The NM does not require volume shift to achieve accurate predictions of compositional and volumetric phase behaviors. The two types of phase behaviors are properly coupled in the NM.
- North America > Canada > Alberta (0.28)
- North America > United States > Texas (0.28)
- North America > United States > California (0.28)
- Research Report > Experimental Study (0.48)
- Research Report > New Finding (0.48)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.91)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Oil sand, oil shale, bitumen (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
Abstract Reservoir fluid properties are very important in reservoir engineering computations such as material balance calculations, well test analysis, reserve estimates, and numerical reservoir simulations. Ideally, this property should be obtained from actual measurements. Quite often, this measurement is either not available, or very costly to obtain. In such cases, empirically derived correlations are used in the prediction of this property. This work focuses on the use an artificial neural network (ANN) to address the inaccuracy of empirical correlations used for predicting oil formation volume factor. In this modeling approach 802 data set collected from the Niger Delta Region of Nigeria was used. The data set was randomly divided into three parts of which 60% was used for training, 20% for validation, and 20% for testing. Both quantitative and qualitative assessments were employed to evaluate the accuracy of the new Artificial Neural Network to the existing empirical correlations. The ANN model outperformed the existing empirical correlations by the statistical parameters used with a lowest rank of 0.855 and better performance plot.
- Asia (1.00)
- Africa > Nigeria > Niger Delta (0.62)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
ABSTRACT Crude oil properties such as viscosity, molecular composition, and saturate, aromatic, resin, and asphaltene (SARA) fractions are crucial parameters for evaluating reservoir quality, producibility, and compartmentalization. In the past, physical and empirical models that relate oil properties to NMR measurements have been developed. How-ever, the existing models are too simplistic to accurately predict properties of crude oils which are complex mixtures of hydrocarbon and non-hydrocarbon molecules. This paper introduces a model-independent technique for quantitative predictions of live-oil properties from NMR measurements. The technique assumes that the physics connecting NMR measurements to oil properties is implicitly contained within a database of NMR and fluid-property measurements made on a representative suite of live oils. The input measurements are mapped to oil properties using a mapping function that is a linear combination of Gaussian radial basis functions. The parameters of the mapping function are determined from the database. The mapping function predicts properties from input measurements made on live oils that are not in the database. To validate the technique, an extensive database of NMR and fluid-property measurements made on live oils at elevated temperatures and pressures was acquired. Viscosities, molecular compositions, and SARA fractions were accurately determined from NMR measurements using the mapping function technique.
- North America > United States (0.46)
- North America > Canada (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.66)
Abstract: Heterogeneity of the resource-shale plays and limited knowledge about the shale petrophysical properties demand detailed core-scale characterization in order to understand field-scale measurements that have poor vertical resolution. Analyses of a set of laboratory measured petrophysical properties collected on 300 samples of the Woodford Shale from 6 wells provided an opportunity to track changes in petrophysical properties in response to thermal maturity and their effect on hydrocarbon production. Porosity, bulk density, grain density, mineralogy, acoustic velocities (Vp-fast, Vs-fast and Vs-slow), mercury injection capillary pressure along with total organic carbon content (TOC), Rock-Eval pyrolysis, and vitrinite reflectance were measured. Visual inspections were made at macroscopic-, microscopic- and scanning electron microscope-scale (SEM) in order to calibrate rock-petrophysical properties with the actual rock architecture. Mineralogically, the Woodford Shale is a silica-dominated system with very little carbonate presence. Crossplot of porosity and TOC clearly separate the lower thermal maturity (oil window) samples from higher thermal maturity (wet gas-condensate window) as porosity is lower at lower thermal maturity. Independent observations made through SEM-imaging confirm much lower organic porosity at lower thermal maturity while organic pores are the dominant pore types in all samples irrespective of thermal maturity. Crack-like pores are only observed at the oil window. Cluster analyses of TOC, porosity, clay and quartz content revealed three clusters of rocks which could be ranked as good, intermediate and poor in terms of reservoir quality. Good correlations between different petro-types with geological core descriptions, along with the good conformance between different petro-types with production data ascertain the practical applicability of such petro-typing. Introduction The Woodford Shale has long been known as the source of most of Oklahoma's hydrocarbon reserves until it emerged as resource play following the huge success of the Barnett Shale play in 2005.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- (14 more...)
Summary There is increasing interest in modeling networks of wells, including subsurface components of complex wells and surface facilities. Such modeling requires setting constraints at various points in the network. Typical constraints are maximum phase flow rates and minimum flowing pressures. A major difficulty in network calculations is determining which of these constraints is active. This paper presents a method that uses slack variables in determining active constraints. The linearized equations of interest generally come in pairs, with each pair consisting of a base equation and a constraint equation. The base equation is the equation that normally applies. The constraint equation replaces it if the constraint is active. Normally, only one of these two equations can be satisfied. The slack variable provides a way to ensure that both are satisfied, regardless of which is active. If the constraint is inactive, the slack variable is added to the constraint equation and accounts for the slack, which by definition is the amount by which the inactive equation is not satisfied. On the other hand, if the constraint is active, the slack variable is instead added to the base equation, and the constraint equation as originally written is satisfied. To obtain this behavior, we define a parameter w and add w times the slack variable to the base equation and (1 − w) times the slack variable to the constraint equation. Thus, if w = 1, the slack variable is added to the base equation, and the constraint is active. On the other hand, if w = 0, the slack variable is added to the constraint equation, and the base equation is active. The slack is always in the inactive equation. There is a w associated with each slack variable. Determining the parameter w is an iterative process. The efficiency of the process is improved by manipulating the network matrix such that we can create a Schur complement that has the slack variables as its unknowns and contains the only references to the w's To determine the slack variables, we need only to work with this matrix, which typically is much smaller than the network matrix. The resulting method is implemented within a general purpose reservoir simulator. Testing of the method in more than 700 cases has shown it to be much more robust than an earlier heuristic procedure.