The Niobrara interval in the Denver-Julesberg (DJ) Basin contains several important unconventional hydrocarbon targets. However, the Niobrara is extensively faulted, which poses challenges for accurately landing and steering laterals in zone. Insight into small faulted structures in the Niobrara using traditional manual fault interpretation techniques is challenging because of the tuning thickness in seismic data. Fault throws less than the tuning thickness are difficult to interpret and incorporate into geosteering plans. Consequently, drillers frequently find themselves out of zone after crossing these small faults. Using independent information about fault locations and throws provided from multiple horizontal wells in the DJ Basin, this paper demonstrates the fault likelihood attribute (Hale, 2013) can resolve fault throws as small as 10 ft, allowing seismic-based well plans and unconventional project economics to be significantly improved.
Traditional geoscience data interpretation workflows in support of well planning can be tedious and time consuming, requiring manual fault picking on seismic profiles in conjunction with horizon tracing and gridding for structural mapping. The emergence of unconventional resource plays requires both more efficient geoscience workflows to support round-the-clock drilling operations and more detailed structural interpretations to help ensure laterals are steered along sweet spots. Pre-drill mapping of small-scale faults is therefore of particular importance for safe operations and helping ensure that lateral wells stay in zone.
Recent advances in fault-sensitive post-stack seismic attributes are changing the way subsurface professionals think about faults and how to map them in 3D space. In particular, the fault likelihood attribute (Hale, 2013) has provided a breakthrough improvement in the quality of seismic-derived fault attributes. Typically, the fault likelihood attribute is used in exploration settings to rapidly generate a broad-scale structural interpretation, being used both as a guide to manual fault interpretation and as input into automated fault extraction algorithms. This paper demonstrates the value of fault likelihood in development settings for assisting the well planning and geosteering process.
We describe a case study to extract meaningful geological information from a modern high-fold seismic dataset in the north-east Delaware basin. The target of the study is the heterogeneous geology of the Bone Spring and Wolfcamp Formations. A database of well data was used to understand the variation in elastic properties in terms of geological changes that include: mineralogy, organic content and the likely onset of over-pressure. The geology was represented by a set of 5 elastic facies: carbonates, calcareous mudstones, siliciclastics, organic-rich and clay-rich shales. The well data were also used to calibrate seismic amplitudes prior to performing a Bayesian pre-stack inversion to solve for estimates of facies and impedances. The results are shown to provide insights into the regional stratigraphic deposition and evolution of the formations, including mapping of discontinuous carbonate and high TOC intervals. The property volumes are the starting point for future predictive geological, formation-pressure and stress models for informing optimal resource exploitation within the study area.
The geology of the Delaware Basin is heterogeneous in both lateral and vertical directions. Understanding the geological complexity is critical for optimizing exploitation strategies in both the Bone Spring and Wolfcamp Formations. Seismic data provide valuable spatial information between and away from well locations, however, the process of extracting the geological information from the seismic amplitudes is non-trivial. In this paper, we describe a state-of-the-art pre-stack seismic inversion study in the north-east Delaware Basin, with the objective of obtaining reliable estimates of the geology across the 380 square mile study area, Figure 1.
The petro-elastic model (PEM) represents an integral component in the closed-loop calibration of integrated four-dimensional (4D) solutions incorporating time-lapse seismic, elastic and petrophysical rock property modeling, and reservoir simulation. Calibration of the reservoir simulation model is needed so that it is not only consistent with production history but also with the contemporaneous subsurface description as characterized by time-lapse seismic. The PEM requires dry rock properties in its description, which are typically derived from mechanical rock tests. In the absence of those mechanical tests, a small data challenge is posed, whereby all necessary data is not available but the value of reconciling seismic attributes to simulated production remains. A seismic inversion-constrained n-dimensional metaheuristic optimization technique is employed directly on three-dimensional (3D) geocellular arrays to determine elastic and density properties for the PEM embedded in the commercial reservoir simulator.
Ill-posed dry elastic and density property models are considered in a field case where the seismic inversion and petrophysical property model constrained by seismic inversion exist. An n-dimensional design optimization technique is implemented to determine the optimal solution of a multidimensional pseudo-objective function comprised of multidimensional design variables. This study investigates the execution of a modified particle swarm optimization (PSO) method combined with an exterior penalty function (EPF) with varied constraints. The proposed technique involves using n-dimensional design optimization to solve the pseudo-objective function comprised of the PSO and EPF given limited availability of constraints. In this work, an examination of heavily and reduced-order penalized metaheuristic optimization processes, where the design variables and optimal solution are derived from 3D arrays, is conducted so that constraint applicability is quantified. While the process is examined specifically for PEM, it can be applied to other data-limited modeling techniques.
Integration of time-lapse seismic data into dynamic reservoir model is an efficient process in calibrating reservoir parameters update. The choice of the metric which will measure the misfit between observed data and simulated model has a considerable effect on the history matching process, and then on the optimal ensemble model acquired. History matching using 4D seismic and production data simultaneously is still a challenge due to the nature of the two different type of data (time-series and maps or volumes based).
Conventionally, the formulation used for the misfit is least square, which is widely used for production data matching. Distance measurement based objective functions designed for 4D image comparison have been explored in recent years and has been proven to be reliable. This study explores history matching process by introducing a merged objective function, between the production and the 4D seismic data. The proposed approach in this paper is to make comparable this two type of data (well and seismic) in a unique objective function, which will be optimised, avoiding by then the question of weights. An adaptive evolutionary optimisation algorithm has been used for the history matching loop. Local and global reservoir parameters are perturbed in this process, which include porosity, permeability, net-to-gross, and fault transmissibility.
This production and seismic history matching has been applied on a UKCS field, it shows that a acceptalbe production data matching is achieved while honouring saturation information obtained from 4D seismic surveys.
Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources.
A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.
The Technical Programme Committee of SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference (APAC URTeC) 2019 invites you to submit a paper proposal and contribute to this flagship event. Paper proposal submission deadline is on 6 May 2019. Authors will be notified of the status of paper proposals by the middle of June 2019 and manuscript submission deadline is on 30 August 2019. A proper review of your paper proposal requires that it contain adequate information on which to make a judgment. Download our instructions guide to assist you with preparing your paper proposal.
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Identification of a prospect is normally done based on seismic interpretation and geological understanding of the area. However, due to the inherent uncertainties of the data we still observe in many cases that all key petroleum system elements are present, but still the drilled prospect is dry. Such failures are mostly attributed to a lack of understanding of seal capacity, reservoir heterogeneity, source rock presence and maturation, hydrocarbon migration, and relative timing of these processes. The workflow described in this paper aims to improve discovery success rates by deploying a more rigorous and structured approach. It is guided by the play-based exploration risk assessment process. The starting point is always that the process is guided by the the basic understanding of a mature kitchen should always be based on a regional scale petroleum systems model. However, while evaluating prospects, the migration and entrapment component of a prospect should always be investigated by means of a locally refined grid-based petroleum system model. The uniquepart of this approach is the construction of a high-resolution static model covering the prospects, which is built by using available well data, seismo-geological trends and attributes to capture reservoir potential. Additional inputs such as fault seal analysis also helps to understand prospect scale migration and associated geological risks. In the regional play and local prospect-scale petroleum system models, geological and geophysical inputs are utilized to create the uncertainty distribution for each input parameter which is required for assessing the success case volume of identified prospects. The evaluated risk is combined with the volumetric uncertainty in a probabilistic way to derive the risked volumetrics. It is further translated into an economic evaluation of the prospect by integrating inputs like estimated production profiles, appropriate fiscal models, HC price decks, etc. This enables the economic viability of the prospects to be assessed, resulting in a portfolio with proper ranking to build a decision-tree leading to execution and operations in ensuing drilling campaigns.
Seismic attributes play an important role during reservoir characterization and three-dimensional (3D) lithofacies modeling by providing indirect insight of the subsurface. Using seismic attributes for such studies has always been challenging because it is difficult to determine a realistic relationship between hard data points (i.e., well information) and a 3D volume of seismic attributes. However, a probability-based approach for 3D seismic attribute calibration with well data provides better results of lithofacies modeling and spatial distribution of reservoir properties. This paper presents a probability-based seismic attribute calibration technique that has been described for 3D lithofacies modeling and distribution. This approach helps in subsurface reservoir characterization and provides a realistic lithofacies distribution model. This approach also helps reduce uncertainty of lithofacies prediction compared to conventional methods of simply using geostatistical algorithms.
Geophysical Reservoir Monitoring GRM systems such 4D seismic are increasingly used in the oil and gas industry because they provide unique and useful information on fluid movement within the reservoir. This information is relevant for many reservoir management decisions; including new well placement, well intervention, and reservoir model updating.
Unfortunately, it has been difficult to estimate the value creation of any data acquisition scheme due to the fact that a multidisciplinary approach is required to model the value that future measurements will imply in future decisions. This assessment requires a common decision making simulation frame work that can integrate the input from geo-modelers, geophysicist and reservoir engineers.
This work presents an example of how a Close Loop Reservoir Management (CLRM) simplification can be used as a framework for simulating NPV changes due to assimilation of production and saturations in a simple toy model. It combines state-of-the-art data assimilation and uncertainty modeling methods with a robust optimization genetic algorithm to calculate NPV improvements due to model update and its relationship with the NPV obtained from the synthetic reservoir.
In this context a simple synthetic model is presented. It recreates a segment of green field under a strong aquifer influence with two discovery wells. The reservoir development requires the selection of 4 well locations at fixed drilling times. The development strategy selection is obtained with the use of a genetic algorithm within the CLRM framework. Subsequently two cases are presented: one of assimilating only production after the first two wells have been drilled, just before deciding the locations of the last two wells; and a second case, in which production and saturation are assimilated at the same time. The saturation map assimilated is assumed to be output of a 4D seismic acquisition. The model update imposes the need of optimally relocate the last two wells which results in a NPV change.
The results show how the obtained NPVs is incremented by the relocation of the last two wells in both cases. A bigger increment is obtained when both, production and saturation are assimilated. In addition, the ensemble improved its forecast capability the most, when saturation assimilation is included. Nevertheless, the ensemble expected NPV decreases after assimilation from the value obtained from the first development strategy optimization; this indicates an optimistic early NPV valuation due to the initial ensemble distributions spread.
The study presents an asset simulation framework that could be used to evaluate data acquisition investments through the systematic modeling of reservoir uncertainties with in a decision oriented focus. This could include the inclusion of additional uncertain model parameters, the insertion of water injector and well conversions, the assimilation of saturations at different intervals, the change on the quality of the saturation maps assimilated, in addition to sensitivity studies of other economic constrains.