Zhou, Xu (Louisiana State Unviersity) | Tyagi, Mayank (Louisiana State Unviersity) | Zhang, Guoyin (China University of Petroleum - Beijing) | Yu, Hao (Southwest Petroleum University) | Chen, Yangkang (Zhejiang University)
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.
3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.
In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.
Lolla, Sri Venkata Tapovan (ExxonMobil Upstream Research Co) | Bailey, Jeffrey (ExxonMobil Development Co) | Costin, Simona (Imperial Oil Resources Ltd) | Hons, Michael (Imperial Oil Resources Ltd) | Liu, Xinlong (Imperial Oil Resources Ltd) | Yam, Helen (Imperial Oil Resources Ltd) | Akhmetov, Arslan (ExxonMobil Canada Properties) | Hayward, Timothy (Imperial Oil Resources Ltd) | Brisco, Colin (Imperial Oil Resources Ltd)
Continuous subsurface surveillance is important for heavy oil in-situ recovery processes where induced stresses in the overburden can compromise the integrity of the wellbores. Wellbore failure may lead to the undesirable loss of fluids into the overburden. In recent years, there has been a rapid growth in the use of Passive Seismic monitoring systems to aid in subsurface surveillance activities, with the ultimate goal of detecting potential integrity issues as early as possible. However, the massive volume of data recorded by these instruments is time-consuming and error-prone to process manually. This paper introduces EMMAA (ExxonMobil Microseismic Automated Analyzer), an automated workflow to reliably process continuous microseismic data, detect subsurface integrity issues, and ultimately reduce the latency in responding to wellbore integrity issues.
A novel cloud-based technology for managing microseismic data is briefly described. The seismic waveforms, recorded by a distributed array of geophone receivers, are automatically analyzed to determine the type and source of subsurface disturbances (
First, novel frequency-domain and deep learning analyses are used to distinguish noisy signals from the seismic waveforms such as compressional and shear waves produced by the events. Next, the location of the event is calculated and its seismic attributes are computed. Finally, the type and severity of the seismic event are determined by an event classifier.
The performance of the automated workflow is examined in the context of accurate detection of casing failures in a heavy oil Cyclic Steam Stimulation (CSS) application. The event features that distinguish casing breaks from other seismic events are described. It is shown that the methodology is able to achieve a high detection rate when back-tested against a historical data-set of known casing failures. False positives are adequately contained by preventing waveforms of electrical or mechanical noise from being processed.
In a production environment, the event processing workflow is run on distributed servers and analyzes triggered seismic data in real-time. Depending on the severity of the microseismic events detected, operators are immediately alerted via email and text messages, so that remedial actions may be swiftly initiated. The utility of this integrated system is further exemplified by the massive reduction in the time taken to detect casing breaks—from up to 36 hours historically, down to less than one hour in most instances.
Extensions of EMMAA that enable the detection of a wide variety of microseismic events are also discussed. These events include surface casing slips that occur at the casing shoe, cement de-bonding events near the wellbores, and events indicative of potential fluid migration in the overburden.
Many investigations have been discussed and it is a well-recognized fact that sonic wave velocity is not only influenced by its rock matrix and the fluids occupying the pores but also by the pore architecture details of the rock bulk. This situation still brings a lack of understanding, and this study is purposed to clearly explain how acoustic velocity and quality factor correlate with porosity, permeability and details internal pore structure in porous rocks.
This study employs 67 sandstone and 120 carbonate core samples collected from several countries in Europe, Australia, Asia, and USA. The measured values are available for porosity
At least eight rock groups are established from rock typing with its Kozeny constant. This constant is a product of pore shape factor
As a novelty, the empirical equations are derived to estimate compressional velocity and quality factor based on petrophysical parameters. Furthermore, this study also establishes empirical equations for predicting porosity and permeability by using compressional wave velocity, critical porosity, and PGS rock typing.
The seismic inversion method using the seismic onset times has shown great promise for integrating real- continuous seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, such frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach, aiming to extend the advantages of onset time approach when infrequent seismic surveys are available.
To analyze the impact of seismic survey frequency on the onset time approach, first, we conduct a sensitivity analysis based on the frequent seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. The calculated onset time maps based on seismic survey data sampled at various time intervals from the frequent data sets are compared to examine the need and effectiveness of interpolation between surveys. Additionally, we compare the onset time inversion with the traditional seismic amplitude inversion and quantitatively investigate the nonlinearity and robustness for these two inversion methods.
The sensitivity analysis shows that using interpolation between seismic surveys to calculate the onset time an adequate onset time map can be extracted from the infrequent seismic surveys. This holds good as long as there are no changes in the underlying physical mechanisms during the interpolation period. A 2D waterflooding case demonstrates the necessity of interpolation for large time span between the seismic surveys and obtaining more accurate model update and efficient data misfit reduction during the inversion. The SPE Brugge benchmark case shows that the onset time inversion method obtains comparable permeability update as the traditional seismic amplitude inversion method while being much more efficient. This results from the significant data reduction achieved by integrating a single onset time map rather than multiple sets of amplitude maps. The onset time approach also achieves superior convergence performance resulting from its quasi-linear properties. It is found that the nonlinearity of the onset time method can be smaller than that of the amplitude inversion method by several orders of magnitude.
Within a single field geophysical survey results always have a significant amount of data with a considerable variability and heterogeneity. This allows to classify geophysical data as a Big Data. Data scientists and software developers are increasingly recommending the use of machine learning techniques for data processing and interpretation. ML algorithms allow one to extract the most complete amount of useful information, reduce time costs, minimize the subjective factor in the decision-making process, etc. Early testing of these approaches began in the 60s, active practical implementation consisted in the 90s due to the large-scale implementation of seismic studies in 3D CDP modification 1. The emergence of new algorithms, modifications of the original data, the development of computational resources support the relevance of this topic at the present time. In seismic data interpretation machine learning approaches provide high performance in the process of automatic horizons picking, fault tracing, seismic facies analysis, sesimic inversion, reservoir prediction, etc. At the stage of seismic facies analysis application of the ML algorythms is especially effective since in the process of multiattribute classification the initial dataset increases severalfold in accordance with the number of calculated attributes 5-7, 9, 10.
Gupta, Harshit (Indian Institute of Technology Delhi) | Pradhan, Siddhant (Indian Institute of Technology Delhi) | Gogia, Rahul (Indian Institute of Technology Delhi) | Srirangarajan, Seshan (Indian Institute of Technology Delhi) | Phirani, Jyoti (Indian Institute of Technology Delhi) | Ranu, Sayan (Indian Institute of Technology Delhi)
Horizons in a seismic image are geologically signficant surfaces that can be used for understanding geological structures and stratigraphy models. However, horizon tracking in seismic data is a time consuming and challenging task. Saving geologist's time from this seismic interpretation task is essential given the time constraints for the decision making in the oil & gas industry. We take advantage of the deep convolutional neural networks (CNN) to track the horizons directly from the seismic images. We propose a novel automatic seismic horizon tracking method that can reduce the time needed for interpretation, as well as increase the accuracy for the geologists. We show the performance comparison of the proposed CNN model for different training data set sizes and different methods of balancing the classes.
Pavlov, Dmitry (Sakhalin Energy Investment Company Ltd.) | Fedorov, Nikolay (Sakhalin Energy Investment Company Ltd.) | Timofeeva, Olga (Sakhalin Energy Investment Company Ltd.) | Vasiliev, Anton (Sakhalin Energy Investment Company Ltd.)
This paper summarizes the results of 3 years collaborative efforts of the Geophysicist, Production Geologist and Reservoir Engineers from the Astokh Development Team and a Geochemist from the LNG plant laboratory on integration of reservoir surveillance and reservoir modelling.
In period 2015 – 2018 a large bulk of geological and field development data was collected in Astokh field, in particular: cased and open hole logs, core, open hole pressure measurements, flowing and closed-in bottom hole pressures, well test data, new 4D seismic surveys (2015, 2018), fluid samples. Since 2016, essential progress was made in oil fingerprinting for oil production allocation in Astokh field. Simultaneously, the need for update of static and dynamic models was matured upon gaining experience in dynamic model history matching to field operational data (rates, pressures, well intervention results). In other words, the need in update of geological architecture of the Astokh reservoir model was matured upon reaching critical mass of new data and experience. To revise well correlation, it was decided to combine different sorts of data, in particular seismic, well logs and core data and reservoir pressures. Different pressure regimes were identified for 3 layers within XXI reservoir. Pressure transient surveys were used for identification of geological boundaries where it's possible and this data was also incorporated into the model. Oil fingerprinting data was used for identification of different layers and compartments. Integration of pressure and oil geochemistry data allowed to identify inter-reservoir cross-flows caused by pressure differential. Based on all collected data, sedimentology model and reservoir correlation were updated based on sequential stratigraphy. As a result, a new static model of main Astokh reservoirs was built, incorporating clinoform architecture for layers XXI-1' and XXI-2. To check a new concept of geological architecture material balance model was used and matched to field data
Integration of geological and field operational data provided a key to more advanced reservoir management and development strategy optimization. Based on updated reservoir model, new potential drilling targets were identified. Also, with new well correlation, water flood optimization via management of voidage replacement ratio was proposed. The completed work suggests essential improvement in reservoir modelling process by inclusion of various well and reservoir surveillance data.
The paper consists of the following sections: Introduction Field geology Field development history Scope of work complete and main results Proposed well correlation update for XXI-1' and XXI-2 layers Integration of well logs, pressure and fluid analysis data Connectivity between layers XXI-S, XXI-1' and XXI-2 Integration of pressure and oil fingerprinting data Connectivity within layers XXI-S, XXI-1' and XXI-2 Results of pressure interference tests Testing of new well correlation concept in material balance model Proposed reservoir correlation updated based on seismic data New geological concept New depositional model Integration of core data Changes in reservoir architecture Conclusion Main results and impact on field development
Field development history
Scope of work complete and main results
Proposed well correlation update for XXI-1' and XXI-2 layers
Integration of well logs, pressure and fluid analysis data
Connectivity between layers XXI-S, XXI-1' and XXI-2
Integration of pressure and oil fingerprinting data
Connectivity within layers XXI-S, XXI-1' and XXI-2
Results of pressure interference tests
Testing of new well correlation concept in material balance model
Proposed reservoir correlation updated based on seismic data
New geological concept
New depositional model
Integration of core data
Changes in reservoir architecture
Main results and impact on field development
Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.
In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.
In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.
The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
Identification of tidal channels fairways is key for predicting behavior of areas at higher risk to water breakthrough or otherwise have a significant impact on the development and monitoring of reservoir performance. However, tidal channels in carbonates are not often easily characterized using conventional seismic attributes. It is important to decipher the complexity of the carbonate tidal channel architecture with integrated multisource data and a variety of approaches.
In this paper, petrological characteristics and petrographic analysis is conducted on well logs and validated carefully using core data. Then, the second step is to compare the carbonate channel systems with modern analogue in Bahama tidal flat and outcrop scales in Wadi Mi'Aidin (Northern Oman). Thereafter, the supervised probabilistic neural network (PNN) and linear regression method were undertaken to detect an additional channel distribution.
The relationship of high porosity with low acoustic impedance appeared mostly in the channel facies which reflects good reservoir quality grainstone channels. Outside these channels, the rock is heavily mud filled by peritidal carbonates and characterized by a high acoustic impedance anomaly with low quality of porosity distribution. The new observation of PNN porosity volume revealed a lateral distribution of the Mishrif carbonate tidal channels in terms of paleocurrent direction and the connectivity. Additionally, the prior information from core data and the geological knowledge indicate a good consistency with classified lithology. These observations implied that Mishrif channels consist of a wide range of lithology and porotype fluctuations due to the impact of depositional environment.
The work enables us to provide a new insight into the distribution of channel bodies, and petrophysical properties with quantification of their influence on dynamic reservoir behavior of the main producing reservoir. This work will not only provide an important guidance to the development and production of this case study, however also deliver an integrated work path for the similar geological and sedimentary environment in the nearby oil fields of Southern Iraq.
Taha, Taha (Emerson Automation Solutions) | Ward, Paul (Emerson Automation Solutions) | Peacock, Gavin (Emerson Automation Solutions) | Heritage, John (Emerson Automation Solutions) | Bordas, Rafel (Emerson Automation Solutions) | Aslam, Usman (Emerson Automation Solutions) | Walsh, Steve (Emerson Automation Solutions) | Hammersley, Richard (Emerson Automation Solutions) | Gringarten, Emmanuel (Emerson Automation Solutions)
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.