A New Approach to Seismic Data Integration During Reservoir Characterization Using Optimal non-Parametric Transformations

The Incorporated Research Institutions for Seismology (IRIS) Seismographs in Schools (SIS) program has supported teachers in the use of educational seismometers and real-time seismic data in the classroom for the past 20 years. To better understand how the seismographs and seismic data are being used in the classroom, we sent a survey to 770 past or current program participants. The survey asked about their seismometer, seismic data use, software use, the impact of seismic data on their classroom, and what additional seismology-related resources or instruction they provide to their students. Four highly-engaged teachers were then recruited from this larger pool for a case study. The purpose of this study was to better understand these highly-engaged teachers, their experiences with seismic instrumentation, software and data in the classroom, how and to what degree seismic data has impacted their curriculum and instruction, and their perceptions of the impact of the SIS program on their students’ understanding of Earth Science concepts. The case study results show that each of the highly-engaged teachers values data driven instruction, instrument science, and the integration of seismic data into the classroom more than just during their earthquake units. They have made it part of everyday class activities by having students be aware of data coming in, noting earthquakes in the news, and helping students learn more about analyzing data for advanced investigations. While there were differences in their implementations, in all cases a critical feature of their engagement with students was use of a variety of seismology-related resources which connected the seismic data to the rest of the curriculum. Thus, the use of seismic data was just one component of their seismology-related teaching. This study also highlights the value of a local sensor, as all four highly-engaged teachers stressed that students as stewards of the seismometer linked them to the science in an engaging and dynamic way. Thus, while the highly-engaged teachers had a primary responsibility to promote learning, their focus on student engagement is also helping to create young citizen scientists.

Fault cubes, salt bodies, sand channel volumes, gas chimney data and hydrocarbon probability cubes, extracted from 3-D seismic are rapidly becoming valuable tools for exploration and field development. Such seismic anomalies can be highlighted using a new technique that combines seismic attributes in a special way. Computer algorithms are developed to search through data volumes looking for specific types of anomalous seismic data using criteria referred to as "meta-attributes." Meta-attributes are an aggregation of a number of seismic attributes combined with the interpreter's insight through a neural network to detect a particular feature. One of the main features of the meta-attribute concept is combining "artificial intelligence" of neural networks with the "natural intelligence" of an interpreter. This leads to a more comprehensive integration of geological, petrophysical and seismic data. Non-linear interrelationships between data as well as knowledge versus geologic features and reservoir properties are defined implicitly at the natural scale. Meta attributes extracted from multiple input seismic volumes and derived attributes are used to predict facies, lithology and hydrocarbon probability, as well as for detecting faults, fractures, channels, gas chimneys or salt bodies. The meta-attribute approach is placed in the historical context, the technology is explained and examples for various seismic object detections problems are given. Evolution of Attribute Technology Seismic attribute technology has been gaining popularity ever since it was introduced by Taner et al (1979). These originally introduced "instantaneous attributes" and the subsequent multi-trace and pre-stack attributes have found many applications in different exploration and field development problems. Figure 1 shows the evolution of attribute technology during the last four decades. The text to the right of the road shows the actual applications and those to the left, the corresponding technologies. In recent years the advent of 3D and 4D technology and increasing computing power led to an explosion in useful attributes. Nowadays there is an increasing need to identify subsets or combinations of attributes that can highlight a given geological or reservoir property most effectively. Figure 2: Evolution of- Attribute Technology (Available in full paper) The first publication in which several attributes were combined for E&P applications was by Aminzadeh and Chatterjee (1985). This was accomplished by first, performing a "principal component analysis" (PCA) to transform the attributes to the "factor space", ensuring they are not correlated. This was followed by "clustering" to highlight gas related bright spots using several attributes in the factor space. Clustering or other conventional statistical tools (regression, cross plots, etc) allow a linear transformation to combine different attributes and compare their respective contribution and role in the classification process. Subsequently, it was suggested by, for example, Wong et al (2003), that in many circumstances, unconventional statistical methods may be required to fully capture and account for the non-linear relationship between the seismic data and reservoir properties. Neural network-based methods, offer one such possibility, allowing non-linear transformation of attributes to establish stronger and more accurate relationship between the seismic data (seismic attributes) and the geological features or reservoir properties (Schuelke et al, 1997 and Aminzadeh and de Groot, 2004).

The prediction of highly heterogeneous reservoir parameters from seismic amplitude data is a major challenge. Seismic attribute analysis can enhance the tracking of subtle stratigraphic features. It is challenging to investigate these subtle features, including channel systems, with conventional-amplitude seismic data. Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. The purpose of this research was to assess the efficiency of an unsupervised self-organizing map (SOM) approach supported by multi-attribute analysis that could improve gas channel detection and seismic facies classification in Serpent Field, offshore Nile Delta, Egypt. As well as evaluates the importance of several available seismic attributes in reservoir characterization rather than analyzing individual attribute volumes. In this study, the single attribute (spectral decomposition attribute) highlighted the gas channel spatial distribution using three distinct frequency magnitude values. Subsequently, we employ principal component analysis (PCA) as an attribute selection method, discovering that combining seismic attributes such as sweetness, envelope, spectral magnitude, and spectral voice as input for SOM reflects an effective method to determine facies. The clustering results distinguish between shale, shaly sand, wet sand, and gas-saturated sand and identify gas–water contact on a 2D topological map (SOM), where each pattern indicates certain facies. This is achieved by associating the SOM outputs with lithofacies determined from petrophysical logs. Reducing exploration and development risk and empowering the geoscientist to generate a more precise interpretation are the ultimate objectives of this multi-attribute analysis.

PP-NFR: an improved hybrid learning approach for porosity prediction from seismic attributes using non-linear feature reduction

The main objective of the reservoir characterization is to provide reservoir property maps to reservoir engineers for the field appraisal. Since that porosity and water saturation affect both seismic and EM attributes, their estimation can reduce the uncertainty of the interpretation and consequently the costly drilling of un-productive reservoirs. This study explains the petrophysical joint inversion (PJI) of seismic and EM attributes to estimate the petrophysical model in terms of porosity and water saturation. Rock physics models are involved as forward models to form a proper link between data input (seismic and EM attributes) and the petrophysical parameters, (porosity and water saturation). According to the formal Bayesian theory, the uncertainty affecting the input data (AI and resistivity models) and the model parameters (porosity and water saturation) is captured by, respectively, data and model covariance matrix. It is assumed Gaussian probability density functions for model parameters and input data. PJI is applied where both seismic attributes and resistivity models show consistent anomalies as evidence of a potential reservoir. PJI objective is to jointly estimate porosity and water saturation from AI (seismic inversion derived), and resistivity (CSEM inversion derived), by using rock physics modeling within Bayesian framework. The result is represented by the porosity and water saturation distribution within the potential reservoir. PJI realizes a multi-physic inversion able to exploit the complimentary information available in the single physic domains, which are seismic and EM. The methodology is applied to a real hydrocarbon exploration scenario to evaluate its contribution to the interpretation phase. 3D volumes of estimated porosity and saturation show how the joint inversion of acoustic impedance and electrical resistivity provide a quantitative description of the reservoir properties and with it a measure of uncertainty, which is consistent with the petrophysical model and observations. Introduction Reservoir characterization objectives are to estimate the petrophysical properties of the prospective hydrocarbon traps and to reduce the uncertainty of the interpretation. In this framework, we present a workflow for petrophysical joint inversion of seismic and EM attributes to estimate the petrophysical model in terms of porosity and water saturation. This study realizes the joint inversion within the probabilistic structure provided by the Bayesian theory. 3D volumes of estimated porosity and saturation, show how the joint inversion of acoustic impedance and electrical resistivity can provide a quantitative description of the reservoir properties and with it a measure of uncertainty, which is consistent with the petrophysical model and observations.

Summary The post-fracture pressure decay (PFPD) technique is a low-cost method allowing for stage-by-stage hydraulic fracture characterization. The analysis of the PFPD data is complex, with data affected by both hydraulic fracture and reservoir properties. Available analysis methods in the literature are oversimplified; reservoir or fracture properties are often assumed to be constant along the horizontal well, and therefore changes in the trend of pressure decay data are attributed to hydraulic fracture or reservoir properties only. Moreover, methods analogous to those applied to the analysis of conventional diagnostic fracture injection tests (DFITs) are often used and ignore critical mechanisms involved in main-stage hydraulic fracture stimulation. A conceptual numerical simulation study was first conducted herein to understand the key mechanisms involved in main-stage hydraulic fracturing. An analytical model was then developed to account for the dynamic behavior of the hydraulic fracture, leakoff, proppant distribution, multiple fractures, and propped- and unpropped-closure events. The analytical model is cast in the form of a new straightline analysis (SLA) method that provides stage-by-stage estimates of the ratio of unpropped fracture surface area to total fracture surface area. The SLA method was validated against numerical simulation results. Moreover, to account for the variation of reservoir properties along the horizontal well, the PFPD model is integrated with DFIT-flowback (DFIT-FBA) tests, performed at some points along the lateral, to obtain a reliable stage-by-stage hydraulic fracture and reservoir characterization approach. The practical application of the proposed integrated approach was demonstrated using PFPD and DFIT-FBA data from a horizontal well completed in 22 stages in the Montney Formation. The numerical simulation study demonstrated that the use of proppant and injection into multiple clusters (creating multiple fractures) results in multiple closure events. The closure process may start early after the pump-in period at a pressure significantly higher than the minimum in-situ stress. Using DFIT-based analytical models, which ignore the presence of proppant, causes significant errors in hydraulic fracture and reservoir property estimation. The PFPD field data examined herein exhibited a similar pressure trend to the numerical simulation cases. The ratio of unpropped fracture surface area to total fracture surface area was determined stage by stage using the PFPD SLA method, constrained by DFIT-FBA data. Engineers can use this information to optimize the hydraulic fracture stimulation design in real time, optimize the well spacing, and forecast the production. The cost and time advantages of this diagnostic method make this approach very attractive.

High Resolution, Simultaneous VSP and Land Seismic Acquisition

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method for automatic horizon picking using multiple seismic attributes and the Markov decision process (MDP). For the design of the MDP model, the decision time and state are defined as the horizontal and vertical spatial position on a seismic image, respectively. The reward function is defined in multi-dimensional feature attribute space. Multiple attributes can highlight different aspects of a seismic image and therefore overcome the limitations of the single-attribute MDP through the cross-constraint of multiple attributes. The optimal decision is made by searching the largest state value function in the reward function space. By considering cumulative reward, the lateral continuity of a seismic image can be effectively considered, and the impacts of abnormal waveform changes or bad traces in local areas for automatic horizon picking can be effectively avoided. An effective implementation scheme is designed for picking multiple reflection horizons. The proposed method has been successfully tested on both synthetic and field data.

Reservoirs and the lateral seal of stratigraphic traps are controlled by the depositional environment or diagenesis. The recognition of facies and lithology from seismic attributes is an effective approach for identifying stratigraphic traps related to the depositional environment. In this paper, the occurrence of stratigraphic traps related to depositional environment in Permian aeolian clastics and Jurassic carbonate-evaporites was studied. To identify these stratigraphic traps, multiple seismic attributes were classified using supervised and unsupervised artificial neural networks (ANNs), which allowed the recognition of seismic facies and lithology. Neural networks are a powerful classification technique, which incorporates multiple attributes into a number of classes to identify sedimentary facies. Two algorithms comprising supervised and unsupervised neural networks are commonly implemented. With a supervised learning algorithm, prior information such as typical facies at the control wells are required to train the multilayer perceptron (MLP) network. With an unsupervised algorithm, only seismic data is input to the neural network, and competitive-learning techniques are employed to classify or self-organize the data based on its internal characteristics. Without prior information, the output classes are not labeled with lithofacies. According to the availability of prior information, supervised and unsupervised learning were applied to recognize dune-playa and carbonate-evaporite combinations, respectively. To characterize the depositional environments, joint interpretation with a geological model is necessary for both supervised and unsupervised classification. Two major findings have been derived from this work. First, the learning technology based on ANNs is effective to recognize sedimentary facies. The microfacies and lithologies identified by both supervised and unsupervised ANNs are very consistent with the drilled wells. Second, the recognition of depositional facies and lithology can characterize the stratigraphic traps in the study areas. Lateral seal plays a key role in stratigraphic traps. Playa siltstone and tight lagoonal limestone constitute the lateral seal in dune-playa and carbonate-evaporite combinations, respectively.

Estimating Vsand and reservoir properties from seismic attributes and acoustic impedance inversion: A case study from the Mansuri oilfield, SW Iran

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

This paper proposes a method for quantitative integration of seismic (elastic) anisotropy attributes with reservoir performance data as an aid in characterization of systems of natural fractures in hydrocarbon reservoirs. This method is demonstrated through application to history matching of reservoir performance using synthetic test cases. Discrete Feature Network (DFN) modeling (Dershowitz et al.1) is a powerful tool for developing field-wide stochastic realizations of fracture networks in petroleum reservoirs. Such models are typically well conditioned in the vicinity of the wellbore through incorporation of core data, borehole imagery, and pressure transient data. Model uncertainty generally increases with distance from the borehole. Threedimensional seismic data provides uncalibrated information throughout the inter-well space. Some elementary seismic attributes such as horizon curvature and impedance anomalies have been used to guide estimates of fracture trend and intensity (P32) (Dershowitz and Herda2) in DFN modeling through geostatistical calibration with borehole and other data. However, these attributes often provide only weak statistical correlation with fracture system characteristics. The presence of a system of natural fractures in a reservoir induces elastic anisotropy that can be observed in seismic data. Elastic attributes such as azimuthally dependent normal moveout velocity (ANMO), reflection amplitude versus azimuth (AVAZ), and shear wave bi-refringence can be inverted from 3D seismic data. Anisotropic elastic theory provides physical relationships among these attributes and fracture system properties such as trend and intensity. Effective elastic media models allow forward modeling of elastic properties for fractured media. A technique has been developed in which both reservoir performance data and seismic anisotropic attributes are used in an objective function for gradient-based optimization of selected fracture system parameters. The proposed integration method involves parallel workflows for effective elastic and effective permeability media modeling from an initial DFN estimate of the fracture system. The objective function is minimized through systematic updates of selected fracture population parameters. Synthetic data cases show that 3D seismic attributes contribute significantly to reduction of ambiguity in estimates of fracture system characteristics in the inter-well rock mass. The method will benefit enhanced oil recovery (EOR) program planning and management, optimization of horizontal well trajectory and completion design, and borehole stability studies.

Sand-lithofacies distributions are a vital component of reservoir characterisation and the generation of successful reservoir models, particularly in thin reservoirs thickness. The integration between seismic attributes and conceptual geologic models is important to identify the sand distributions and reservoir geometries. The aim of this work is illustrating the uses of seismic data in revealing the structural and stratigraphic elements of two main reservoir units. It is important to identify and list the attributes, which can be involved in analysing the reservoirs and their degree of involvement in the characterisation of the hydrocarbon accumulation zones. The study was carried out for the reservoirs: Abu Roash C and Abu Roach E in Sitra oil and gas concession, which is located in the northern part of the Egyptian Western Desert. Four seismic attributes are used: 1- Coherence (Variance) 2- 3D Curvature attributes were used for resolving mainly the structural features and also the stratigraphic features 3- Instantaneous phase 4- Instantaneous frequency attributes were used mainly for resolving the stratigraphic features. Through the integration of selected seismic attributes with post-stack seismic data, reservoir distributions and architectures are accurately delineated for the Upper Cretaceous reservoir (Abu Roash ‘C’ and ‘E’). Five wells were used to match the results of the seismic attributes and establish the regional trends of reservoir parameters. The integration of wells data, conventional seismic interpretation and extracted the relevant seismic attributes help in reaching a robust geologic model for the reservoirs of interest. Stratigraphic and structural interpretation objectives based on extracting the seismic attributes for reservoir characterisation become more dominant, as they go deeper in seismic data, to derive more subsurface geologic data than traditional interpretation. 3D structure model of the concern area was established by interpreting the available seismic data as both reservoirs implicated by two fault trends: NW-SE and ENE-WSW. By the end a 3D geological model was defined based on the results of the conventional interpretation of the seismic and seismic attributes to reflect all structural and stratigraphic elements of the two reservoir units.

Reservoir characterization (RC) is a process of finding petrophysical properties of the subsurface mainly from the seismic and well-log data. The nonlinear and heterogeneous nature of the subsurface is the major bottleneck in estimating the reservoir properties. In the past two decades, the RC has eventually turned out to be an interdisciplinary field of research involving computational science, signal processing, geostatistics, and geophysics. This article provides the interdisciplinary perspective in RC focusing on the applications of signal processing and machine learning (ML). We provide an account of various state-of-the-art algorithms while categorizing them into three stages in an RC framework: preprocessing, prediction, and postprocessing. RC has been known to be a highly data-driven problem. Huge volumes of seismic and well-log data are cleverly integrated by experts to decipher the subsurface properties. Some of the anomalies may lead to the existence of a potential reservoir. The signal processing tools are primarily required for information matching, preprocessing for noise and artifacts, and postprocessing for removing irregularities in the prediction, whereas the ML tools are required to map the seismic data to well logs. This article provides a comprehensive study on the recent advances in RC involving seismic volumes and well logs.

Natural fractured carbonate reservoir is extremely challenging in terms of reservoir characterization due to its high heterogeneity of reservoir property and also of its low oil recoveries. This paper will show how the integration of seismic data and well data had helped significantly for the well placement and also in the well completion stage.The datasets that are available and being used in this process are 3D seismic, seismic Inversion, seismic attributes, conventional log and borehole image log. All these data are being utilized in an integrated way to characterize the fractures behavior of the reservoir.