Improved Reservoir Characterization Research Articles

Theory-guided data science-based reservoir prediction of a North Sea oil field

Data science-based methods, such as supervised neural networks, provide powerful techniques to predict reservoir properties from seismic and well data without the aid of a theoretical model. In these supervised learning approaches, the seismic-to-rock property relationship is learned from the data. One of the major factors limiting the success of these methods is whether there exists enough labeled data, sampled over the expected geology, to train the neural network adequately. To overcome these issues, this paper explores hybrid theory-guided data science (TGDS)-based methods. In particular, we build a two-component model in which the outputs of the theory-based component are the inputs in the data science component. First, we simulate many pseudowells based on the well statistics in the project area. The reservoir properties, such as porosity, saturation, mineralogy, and thickness, are all varied to create a well-sampled data set. Elastic and synthetic seismic data are then generated using rock physics and seismic theory. The resulting collection of pseudowell logs and synthetic seismic data, called the synthetic catalog, is used to train the neural network. The derived operator is then applied to the real seismic data to predict reservoir properties throughout the seismic volume. This TGDS method is applied to a North Sea data set to characterize a Paleocene oil sand reservoir. The TGDS results better characterize the geology and well control, including a blind well, compared to a solely theory-based approach (deterministic inversion) and a data science-based approach (neural network using only the original wells). These results suggest that theory and data science can complement each other to improve reservoir characterization predictions.

Reservoir microfacies analysis exploiting microscopic image processing and classification algorithms applied to carbonate and sandstone reservoirs

Two case studies are presented that apply a new quantitative micro-facies analysis method to multiple thin section photographs from the carbonate and sandstone reservoirs of two giant fields: (1) the Permo-Triassic carbonate reservoir formation from the South Pars gas-condensate field (offshore Iran); and (2) the Fasila sand reservoir of Middle Pliocene age from the Shah Deniz Field (offshore Azerbaijan). An essential part of comprehensive microfacies analysis of reservoir rocks is to reliably identify, characterize and quantify microscopic features including matrix components, mineral grains, rock fabrics, porosity, fossil contents and the products of diagenesis. This can be achieved by a combination of image processing techniques to assist in distinguishing paleoenvironments and depositional settings of porous and permeable formations. These techniques, applied to the case studies, are shown to effectively improve reservoir characterization for both carbonate and clastic reservoirs Image processing algorithms have to overcome several hurdles to achieve consistent and repeatable microfacies analysis and this is best conducted by exploiting several algorithms focus on specific recognition tasks. Initially, a histogram equalization algorithm is applied to adjust the quality of thin section images to make them consistently comparable with reference photographs. Microfacies analysis is then conducted on the adjusted images in steps to distinguish matrix texture, grain and pore-space boundaries and micro-fossil components. This is achieved by applying multiple functional image processing algorithms and sensitivity analysis sequentially targeting specific microfacies characterization objectives. Accurate grain size is quantified by a customized Graphical User Interface (GUI) algorithm. Pore detection and quantified two-dimensional porosity volumes are calculated for multiple thin-section images with a K-means clustering of two measurements extracted from color image space analysis. Color image data is also used to specifically distinguish the different minerals constituting the rock matrix, cement, and the form of the pore space in two dimensions. This enables these features to be consistently visualized and displayed in scatter plots that determine the precise microfacies to which each thin section belongs. Applying these techniques to the two case studies demonstrates how they can objectively and quantitatively provide microfacies analysis for carbonate and silicate reservoirs.

Estimating reservoir geomechanical parameters for enhanced reservoir characterization: case study of “Tobi” field, Niger Delta

Reservoir geomechanical parameters in “Tobi” Field, Niger Delta were estimated using well log data with the intention of improving reservoir characterization and establishing stress regime of the field. A suite of well log data were interpreted, reservoirs were identified and correlated across three (3) wells and their geomechanical parameters computed from empirical relations. Three (3) reservoir units were delineated from these wells that falls within the hydrocarbon windows (Agbada Formation). We estimated several geomechanical parameters, which include, but is not limited to, Young’s modulus, Poisson’s ratio, bulk compressibility, unconfined compressive strength (UCS), shear modulus, heat production, vertical stress (overburden), minimum horizontal stress, maximum horizontal stress, shear stress normal to fault plane, shear stress in direction of dip, and shear stress in direction of strike. The bivariate crossplots of heat production and Young’s modulus indicated that the amount of heat produced by the shaly materials affects the elastic properties of the reservoir units and flow rate. The crossplots of Poisson’s ratio versus compressional velocity are expressly well-defined and exponentially decaying with the compressional velocity, indicating an inverse relationship between the two (2) parameters. For stress tensor analysis, the study confirmed the defining condition of Sv > SHmax > Shmin as normal fault, which is fault regime prevalent in the study area, in line with Andersen’s classification. Rotation of principal stresses on fault plane showed negative shear stress in strike direction. This research concluded that geomechanical parameters of hydrocarbon reservoirs in “Tobi” field are diagnostic in reservoir characterization and in establishing the type of faults in reservoirs.

An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study

AbstractThe main reservoir in Huizhou sub-basin is Zhujiang Formation of early Miocene age. The petrophysical analysis shows that the Zhujiang Formation contains thin carbonate intervals, which have good hydrocarbon potential. However, the accurate interpretation of thin carbonate intervals is always challenging as conventional seismic interpretation techniques do not provide much success in such cases. In this study, well logs, three-layer forward amplitude versus offset (AVO) model and the wedge model are integrated to analyze the effect of tuning thickness on AVO responses. It is observed that zones having a thickness greater than or equal to 15 m can be delineated with seismic data having a dominant frequency of more than 45 Hz. The results are also successfully verified by analyzing AVO attributes, i.e., intercept and gradient. The study will be helpful to enhance the characterization of thin reservoir intervals and minimize the risk of exploration in the Huizhou sub-basin, China.

Predicting field production rates for waterflooding using a machine learning-based proxy model

Waterflooding, during which water is injected in the reservoir to increase pressure and therefore boost oil production, is extensively used as a secondary oil recovery technology. Tracking the extent and efficacy of waterflooding (i.e., fluid distributions) is a primary task of reservoir engineers and is traditionally achieved by running full reservoir models. In this work, we design and implement a proxy model using a conditional deep convolutional generative neural network (cDC-GAN), which can be used to quickly calculate the dynamic fluid distribution of a heterogeneous reservoir under waterflooding. Zero-sum game theory is the basis for the cDC-GAN, which includes a pair of generative discriminative models. The generative model tries to learn the relationship between input and output and makes the generated output as close as possible to the training data, while the discriminative model tries to distinguish the fake output and the real data used for training, such that the cDC-GAN learns the real data distribution at the end. In our cDC-GAN formulation, the reservoir properties (permeability distribution in this research) and forecast time information are treated as input, and water saturation is the desired output. The reservoir fluid production rate can be calculated based on the material balance principle. The most significant contribution of this work resides in training a cDC-GAN proxy model to accurately predict fluid saturation. A cDC-GAN has several advantages over the traditional full-model based workflow. First, the model parameters estimated from history matching help to improve reservoir characterization. Second, this proposed proxy model can predict the water and oil saturation distributions simultaneously, which can be used to calculate the water and oil flow rates. Third, this proposed proxy model can be used for waterflooding optimization and uncertainty analysis with far less computational effort than with the traditional method, which uses a reservoir simulator.

Distribution of geomechanical units constrained by sequence stratigraphic framework: Useful data improving reservoir characterization

Rock mechanical studies of a hydrocarbon reservoir play a fundamental role before as well as after drilling and during different stages of a field lifecycle. This research focuses on the discrimination of geomechanical units (GMUs) in the Asmari Formation based on sedimentological and geomechanical properties. Sedimentological properties such as microfacies, diagenesis, texture, facies index (FI) and porosity distribution (pore size, pore shape, pore count and pore aspect ratio) and geomechanical parameters such as uniaxial compressive strength (UCS), fracture toughness (KIc), Biot's coefficient and brittleness index were considered. Sedimentological and diagenetic features and porosity were quantified based on FI concepts that was used for quantitative comparison with geomechanical data. Five GMUs were distinguished in the Asmari Formation that reflect ductile to brittle characteristics based on FI, UCS and KIc cross plots. According to the existing mechanical heterogeneities, the formation was hierarchically subdivided in zones by GMUs allowing to simplify its architecture. Since GMUs are restricted to borehole locations, by linking the latter to the sequence stratigraphic framework, the GMUs can be predicted at field scale because each GMU displays a specific position in the sequence stratigraphic succession. Finally, interpretation of mechanical units within sedimentary cycles and their hierarchy allows predicting engineering operations and propagate geomechanical dataset in the field.

The nature and significance of sand intrusions in a hydrocarbon-rich fold and thrust belt: Eastern Carpathians Bend Zone, Romania

Sand intrusions are commonly associated with hydrocarbon-bearing clastic reservoirs and they can host a significant part of the reserves. They modify the depositional sandbody architecture, and can have an impact on the petroleum system elements. Their significance and complex architecture is difficult to assess in the subsurface due to limited resolution of seismic or core data. Outcrop analogues are key to better understand these complex reservoirs. In the Eastern Carpathians Bend Zone, the Oligocene and lower Miocene succession offer spectacular exposures of deep-water siliciclastic rocks modified by remobilization and sand-intrusion processes. These rocks are also present in producing fields in this area. Sills can be as thick as the depositional sandstones ( c. 4 m), and sill-dominated outcrops can provide a net:gross (net sandstone to shale ratio) of about 35–50%. We quantified their architecture, in a study of the timing and mechanism of emplacement, their relationship to regional tectonics and their impact on reservoir connectivity. The intrusions are interpreted to be syn-tectonic, related to the major mid-Miocene compression in this part of the Carpathian Mountain Belt. The intrusions lead to a large increase in vertical and horizontal connectivity. Our results will help to improve reservoir characterization and production in these reservoirs.

Application of hydraulic flow units\u2019 approach for improving reservoir characterization and predicting permeability

It is highly essential to characterize the reservoir accurately as possible to implement enhanced oil recovery and development scenarios. Determining complex variations in pore geometry aids in identifying rock types with similar fluid flow properties. Traditional classification of rock types is based on geologic observations and empirical relations between porosity and permeability. However, for a given porosity, permeability can vary by several orders of magnitude. This reveals that porosity only cannot interpret the permeability variation. The proposed approach to define the existence of different rock types in the reservoir is the “hydraulic flow units’ approach.” The approach aims to enhance the reservoir characterization and predict permeability in un-cored wells to reduce the cost of drilling core samples. The approach involves dividing the reservoir into hydraulic flow units (HFUs). It introduces the concept of reservoir quality index and flow zone indicator (FZI) to identify the HFUs. Each unit is imprinted by certain FZI and said to have similar geological and petro-physical properties. Validation of results was done by integrating the HFUs with core description results—particularly samples’ grain sizes—to ascertain the accuracy of the approach. FZI was then integrated with well logs using multiple regression analysis in order to train the logs to recognize the hydraulic flow units in case of absence of core. A regression model was developed for each flow unit, from which FZI can be estimated from logs only. FZI was then correlated with permeability to compute permeability. Results showed that four HFUs exist in the reservoir. Four categories of grain sizes were identified from core analysis. This emphasized the accuracy of the proposed technique. Besides, integration between core data and well-logging ones showed high degree of correlation between well logs and FZI. Expanding this correlation aids in predicting permeability in un-cored well.

Improved reservoir characterization by employing hydraulic flow unit classification in one of Iranian carbonate reservoirs

Capillary pressure curves is of great importance in reservoir characterization. Due to the reservoir heterogeneity, no single capillary pressure curve can be used for the entire reservoir depth. This paper aims to examine the application of different techniques of hydraulic flow unit (HFU) classifications in overcoming the extreme heterogeneity of the reservoir in order to improve normalization of capillary pressure in one of the Iranian carbonate oil reservoirs. In this study, well log, routine and specific core analysis lab data have been used to identify flow units by employing different techniques in a heavy oil carbonate reservoir in the south of Iran. These techniques include gamma ray and density log method, flow zone indicator, capillary pressure curves and Winland parameter. Then, mercury injection data has been used by employing Desouky's method to normalize capillary pressure curves for each identified flow unit. According to this study HFU classification significantly improved normalizing of capillary pressure curves. Cited as : Rafiei, Y., Motie, M. Improved reservoir characterization by employing hydraulic flow unit classifification in one of Iranian carbonate reservoirs. Advances in Geo-Energy Research, 2019, 3(3): 277-286, doi: 10.26804/ager.2019.03.06

Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.

Generation of synthetic dielectric dispersion logs in organic-rich shale formations using neural-network models

Dielectric dispersion (DD) logs acquired in subsurface geologic formations generally are composed of conductivity ([Formula: see text]) and relative permittivity ([Formula: see text]) measurements at four discrete frequencies in the range of 10 MHz to 1 GHz. Acquisition of DD logs in subsurface formations is operationally challenging, and it requires a hard-to-deploy infrastructure. We developed three supervised neural-network-based predictive methods to process conventional, easy-to-acquire subsurface logs for generating the eight DD logs acquired at four frequencies. These predictive methods will improve reservoir characterization in the absence of a DD logging tool. The predictive methods are tested in three wells intersecting organic-rich shale formations of the Permian Basin and the Bakken Shale. The first method predicts the eight dispersion logs simultaneously using a single artificial neural network (ANN) model, whereas the second method simultaneously predicts the four conductivity dispersion logs using one ANN model, followed by simultaneous prediction of four permittivity dispersion logs using a second ANN model. The third method sequentially predicts the eight dispersion logs, one at a time using eight sequential ANN models, based on a predetermined ranking of the prediction accuracy for each of the eight DD logs when simultaneously generated. Considering that the conventional and DD logs are recorded more than 10,000 ft deep in the subsurface using logging tools that are run at different times in rugose boreholes for sensing the near-wellbore geologic formation, the data used in this predictive work is prone to noise and biases that tend to adversely affect the prediction performances of the proposed methods. In terms of normalized root-mean square error (Nrms error), the prediction performances of the second predictive method are 8.5% worse and 6.2% better for the conductivity and permittivity dispersion logs, respectively, as compared with those of the first predictive method. The third method has best prediction performance for permittivity dispersion logs, which is 0.089 in terms of the Nrms error.

An adaptive neuro-fuzzy inference system for prediction of hydraulic flow units in uncored wells: a carbonate reservoir

ABSTRACTGeoscientists have always sought various approaches to improve reservoir characterisation by compartmentalising the depth interval into subsections with the highest consistency in pore throat size and distribution. Hydraulic flow units have demonstrated success in segmenting the depth interval of interest into subsections with distinguishable rock and fluid properties. At the primary stage, flow zone indicator values are calculated from core data within the reservoir of interest. A flow zone indicator is an acceptably unique measurement of the flow character of a reservoir interval, giving the relationship between petrophysical properties at the pore scale, like tortuosity and surface area, and the formation scale, say porosity and permeability. The next step segments the reservoir into more accurately delineated depth intervals, or hydraulic flow units. Several statistical approaches have been used successfully to group data into subsections of high similarity and consistency, herein referred to as hydraulic flow units. In this paper, a robust method was proposed for the prediction of flow zone indicators in uncored wells, which may lead to advances in reservoir management, saving a considerable amount of revenue merely by accurately predicting depth interval flow properties without the need for expensive coring operations. The results of this study show that adaptive neuro-fuzzy inference systems can be used with higher levels of confidence to model the unknown, but invaluable, data in uncored but logged wells. The results of this study proved the success of machine-learning approaches in identifying underlying trends and relationships within the data, as well as predicting unknown properties based on training data validated by blind test data. This study shows that soft computing and machine-learning approaches can be used to prognosticate the underlying hydraulic flow units based on well log responses in carbonate reservoirs.

Prediction of vertical permeability and reservoir anisotropy using coring data

Most reservoirs have different degrees of permeability anisotropy. Optimization of recovery is crucially dependent on the reservoir quality and anisotropy. The ratio of vertical and horizontal permeability is important when reservoir anisotropy (Kv/Kh) and heterogeneity cannot be neglected. Therefore, an accurate knowledge of vertical and lateral permeability distribution is essential for better reservoir characterization. This work uses routine core data for analysis to develop new correlations and characterization of a sandstone reservoir under development. The two main goals of this study are to use core data to (1) develop correlations capable of predicting vertical permeability from horizontal permeability or mean hydraulic radius and (2) develop another correlation capable of predicting the permeability anisotropy ratio (Kv/Kh) using effective porosity data. To accomplish the objectives of this project, various petrophysical properties were experimentally measured for 112 core samples extracted from an actual sandstone reservoir. The measurements included vertical permeability, horizontal permeability, effective porosity and saturations of oil and water. Applications of the developed results of this study help enhancing the prediction of vertical permeability, improving reservoir characterization and providing better simulation studies.

Vertical-horizontal permeability correlations using coring data

Most reservoirs have different degree of permeability anisotropy. Optimization of recovery is crucially dependent on the reservoir quality and ansitropy. The ratio of vertical and horizontal permeability is important when reservoir anisotropy (Kv/Kh) and heterogeneity cannot be neglected. Therefore, an accurate knowledge of vertical and lateral permeability distribution is essential for better reservoir characterization.This work uses routine core data for analysis to develop new correlations and characterization of a sandstone reservoir under development. The two main goals of this study are to use core data to (1) develop correlations capable to predict vertical permeability from horizontal permeability or mean hydraulic radius and (2) develop another correlation capable to predict the permeability anisotropy ratio (Kv/Kh) using porosity data.To accomplish the objectives of this project, various petrophysical properties were experimentally measured for 112 core samples extracted from an actual sandstone reservoir. The measurements included vertical permeability, horizontal permeability, effective porosity and saturations of oil and water.The data derived were used to develop new and improved correlations capable to predict vertical permeability from horizontal permeability for the zones that were analyzed. In addition, another correlation is developed to predict anisotropy ratio from effective porosity.Applications of the developed results of this study help enhancing the prediction of vertical permeability, improving reservoir characterization and providing better simulation studies.

Technology Focus: Mature Fields and Well Revitalization (January 2019)

Technology Focus Mature brownfields contribute significantly to global oil production. A study of the papers presented during the past year at various SPE conferences reveals a continuation of the trend to put in massive effort to minimize decline from mature brownfields. This has been attempted through several means, such as improving well integrity and judicious well interventions to restart production from idle wells. One paper discusses a conceptual platform where all stakeholders (operator and service providers) can share and formulate a win/win strategy wherein operational efficiency and success rate of an activity are the main drives. As an offshoot of such well-intervention activities, low-resistivity pay zones, either shallower or deeper with respect to the main pay and hitherto considered unattractive, have been identified and contribute significantly in sustaining production decline. There is an all-around emphasis on characterizing highly compartmentalized and difficult reservoirs that have thin reservoir sands defying well-to-well correlations because of a lack of marker shales. In some cases, the presence of conductive minerals such as pyrites or siderites further masks the presence of hydrocarbons in conventional log analysis. In addition to efforts on improved reservoir characterization, optimizing ongoing waterfloods is an area where focused efforts are yielding rich dividends. These involve locating the bypassed oil and addressing unswept areas through reservoir surveillance and modeling. Some operators also have focused on innovative enhanced-oil-recovery techniques in mature brownfields to enhance production. Recommended additional reading at OnePetro: www.onepetro.org. SPE 186362 Extending Life of Liquid-Loaded Gas Wells Using Velocity-String Application: Case Study and Candidate Selection by Satria Andrianata, VICO Indonesia, et al. SPE 188443 Novel Reservoir-Modeling and Experimental-Design Approach To Tackle the Challenge of Modeling Highly Compartmentalized Reservoirs Under Large Uncertainties in a Mature Offshore Field, Malaysia by A. Sulaksana, Sabah Shell, et al. SPE 188770 Fluid-Front Monitoring Using Smart and Safe Pulsed-Neutron Technology on Slickline by Rahul Grover, Schlumberger, et al.

Results of hydraulic fracturing design improvements and changes in execution strategies for unconventional tight gas targets in the Cooper Basin, Australia

Tight gas stimulations in the Cooper Basin have been challenged by strike–slip to reverse stress regimes, adversely affecting the hydraulic fracturing treatment. These stress conditions increase borehole breakout and affect log and cement quality, create more tortuous pathways and near-wellbore pressure loss, and reduce fracture containment. These factors result in stimulation of lower permeability, low modulus intervals (e.g. carbonaceous shales and interbedded coals) versus targeted tight gas sands. In the Windorah Trough of the Cooper Basin, several steps have been employed in an ongoing experiment to improve hydraulic fracturing results. First, the wellbore was deviated in the maximum horizontal stress direction and perforations shot 0 to 180° phased to better align the resulting hydraulic fractures. Next, existing drilling and logging-while-drilling data were used to train a machine learning model to improve reservoir characterisation in sections with missing or poor log data. Finally, diagnostic fracture injection tests in non-pay and pay sections were targeted to specifically inform the machine learning model and better constrain permeability and stress profiles. It is envisaged that the improved well and perforation alignment and better targeting of intervals for the fracturing treatment will result in lowered tortuosity, better fracture containment, and higher concentrations of localised proppant, thereby improving conductivity and targeting of desired intervals. The authors report the process and results of their experimentation, and the results relative to the offsetting vertical well where a typical five-stage treatment was employed.

Big Data analytics in oil and gas industry: An emerging trend

This paper reviews the utilization of Big Data analytics, as an emerging trend, in the upstream and downstream oil and gas industry. Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume, variety, velocity, veracity, value, and complexity. With the recent advent of data recording sensors in exploration, drilling, and production operations, oil and gas industry has become a massive data intensive industry. Analyzing seismic and micro-seismic data, improving reservoir characterization and simulation, reducing drilling time and increasing drilling safety, optimization of the performance of production pumps, improved petrochemical asset management, improved shipping and transportation, and improved occupational safety are among some of the applications of Big Data in oil and gas industry. Although the oil and gas industry has become more interested in utilizing Big Data analytics recently, but, there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry. Furthermore, quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.

Technology Update: Optimizing Well Productivity: Horizontal Logs Unlock Critical Geologic Knowledge

Technology Update With drilling on the rise, US oil production is expected to continue to increase. However, many industry professionals are concerned that the “brute force” model, which has seen a massive influx of capital flow into completion and hydraulic fracturing operations, is not an economically sustainable business model. There is a growing belief that better understanding of the effectiveness of completion designs and hydraulic fracturing strategies will require far greater subsurface geological understanding. Moreover, continued well productivity improvements will only be realized through improved reservoir characterization, and by applying formation petro-physical and geomechanical information to optimize methods of drilling, completing, and stimulating wells. There has been considerable media attention focused on how the industry is drilling wells longer and more quickly, as well as applying higher-intensity fractures. Until recently, however, there has been less emphasis on constructing productive wells with less capital. Traditional vertical well tool conveyance methods effectively addressed industry logging requirements for almost a century, but in today’s “horizontal” world, conventional methodologies are either not applicable or expensive and risky. Essentially, the multibillion-dollar logging market has not quite been turned upside down, but has toppled onto its side. Using available industry data, the low-hanging fruit in the growing “big data” boom, geosciences departments have labored to build accurate high-resolution geological models. Given the nonhomogeneous nature of the geology in unconventional reservoirs, applying vertical offset well logs and interpolating field reservoir models is generally accepted as a good start, but hardly the complete solution. To develop an accurate reservoir model, the heterogeneous nature of shale plays requires far greater data resolution than most operators appreciate. If operators are going to efficiently exploit every stage of their wellbore, they need accurate, measured formation insights along every foot of the horizontal wellbore. Geometric completions and fracturing strategies are not practical or effective in these unpredictable heterogenous environments. Along the wellbore, operators are encountering highly variable rock properties that affect fracturing efficiencies, and result in poor or unexpected well production results. This trend is forcing producers to evaluate stage-by-stage production contributions. They are finding that large portions of their wells are not contributing. There is a growing realization that improved well results that are repeatable and predictable are only going to come about by enhancing production at every stage in the wellbore. This requires engineered completions programs and tailored fracturing strategies that account for the considerable rock heterogeneity along the lateral.

Qualitative time-lapse seismic interpretation of Norne Field to assess challenges of 4D seismic attributes

Interpretation of time-lapse (or 4D) seismic data in terms of reservoir changes due to production posed many challenges in the Norne Field as the field experienced intense production activity from 1997 to 2006. For some segments within the field, fluid movement and pressure changes have approximately the same degree of impact and possibly opposite effects on the seismic data. Moreover, hardening anomalies could be caused by the increase in water saturation or gas going back to solution, while softening anomalies could be related to the increase in pore pressure or the decrease in fluid bulk modulus following the injection of gas. Therefore, for time-lapse seismic analysis to be most effective and less erroneous, different seismic attributes must be addressed to infer reservoir changes caused by production activity such as seismic amplitude and impedance derived by seismic inversion. In the present work, we analyze the challenges of 4D seismic interpretation in the Norne benchmark case. Our study indicates that acoustic impedance differences derived by a 4D model-based inversion provide an increase in vertical resolution compared to standard seismic amplitude differences. We also present a comparison between results of 4D model-based and colored inversions to evaluate the confidence of inversion anomalies. As this is a benchmark case, this study can be considered to enrich the discussions over qualitative and quantitative time-lapse seismic interpretation and to improve reservoir characterization.

Data assimilation of a legacy 4D seismic in a brown field

The quantitative use of four-dimensional (4D) seismic data in an assisted history matching process is still a major challenge in the industry. The problem becomes even more challenging when the quality of the seismic is deficient. Legacy 4D seismic data, i.e. 4D data from seismic acquisitions that were not originally parameterized for time-lapse processing, are typically regarded as not suitable for quantitative approaches. On the other hand, this kind of data are very common in the petroleum industry. Therefore, there is a practical interest in determining whether these data can bring useful quantitative information to improve reservoir characterization through assisted history matching. In this work, we investigate the use of low-quality 4D seismic data (legacy surveys with low repeatability) in an ensemble data assimilation process. We use data from a carbonate reservoir in Campos Basis with a long production history (38 years). The seismic data are the acoustic impedance from a joint 4D inversion. To overcome the overall low quality of the data, we propose to assimilate the seismic data only within the interpreted anomalies regions. We compare the data assimilation results considering only production data and different configurations combining production and seismic data. The results show that we were able to simultaneously assimilate production and seismic data. In our best case, we obtained a production data match with the same quality of a case using only production data, while significantly improving the seismic data match.