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Abstract Fracture growth in layered formations with depth-dependent properties has been a topic of interest amongst researchers because of its critical influence on well performance. This paper revisits some of the existing height-growth models and discusses the evaluation process of a new and modified model developed after incorporating additional constraints.The net-pressure is the primary driver behind fracture propagation and the pressure distribution in the fracture plays an important role in vertical propagation, as it supplies the necessary energy for fracture advancement in the presence of opposing forces. The workflow adopted for this study included developing a preliminary model that solves a system of non-linear equations iteratively to arrive at fracture height versus net pressure mapping. The theoretical results were then compared to those available in the literature. The solution set was then extended to a 100-layer model after incorporating additional constraints using superposition techniques.The predicted outcomes were finally compared to the fracture height observations made in the field on several treatments. A reasonable agreement between model-predicted and observed height was observed when a comparison between the two was made, for most cases.The majority of these treatments were pumped in vertical wells, at low injection rates of up to 8.0 bbl/min (0.021 m/s) where net pressures were intentionally restricted to 250 psi (1.72 MPa) in order to prevent fracture rotation to the horizontal plane.The leak-off was minimal given the low permeability formations. In some cases, however, the pumping parameters and fluid imparted pressure distribution appeared to dominate. Overall, it was apparent that for a slowly advancing fracture front, which is the case in low injection rate treatments, the fracture height could be predicted with reasonable accuracy. This condition could also be met in high rate treatments pumped down multiple perforation clusters such as in horizontal wells, though fracture-height measurement may not be as straightforward as in vertical wells. The model developed under the current study is suitable for vertical wells where fracture treatments are pumped at low injection rates. The solid-mechanics solution that is presented here is independent of pumping parameters and can be readily implemented to assist in selection of critical design parameters prior to the job, with a wide range of applicability worldwide.
Abstract Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.
Abstract Full-physics models in history matching and optimization can be computationally expensive since these problems usually require hundreds of simulations or more. We have previously implemented a physics-based data-driven network model with a commercial simulator that serves as a surrogate without the need to build the 3-D geological model. In this paper, we reconstruct the network model to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley (SJV) for rapid history matching and optimization. The reservoir is simplified into a network of 1-D connections between well perforations. These connections are discretized into grid blocks and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent 2-D Cartesian model is designed where rows correspond to the above-mentioned connections. Thereafter, the history matching can be performed with the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after history matching is then employed for well control optimization. The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the SJV. History matching result shows that the network model honors field-level production history and gives reasonable matches for most of the wells, including pressure and flow rate. The calibrated ensemble from the last iteration of history matching yields a satisfactory production prediction, which is verified by the remaining historical data. For well control optimization, we select the P50 model to maximize the Net Present Value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation non-equilibrium, and strong capillary pressure. Unlike traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model due to the employment of much fewer grid blocks. To our knowledge, this is the first time this physics-based data-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of commercial simulator makes it feasible to be further extended for complex processes, e.g., thermal or compositional flow. It serves as an useful surrogate model for both fast and reliable decision-making in reservoir management.
Soares, Ricardo Vasconcellos (NORCE Norwegian Research Centre and University of Bergen (Corresponding author) | Luo, Xiaodong (email: firstname.lastname@example.org)) | Evensen, Geir (NORCE Norwegian Research Centre) | Bhakta, Tuhin (NORCE Norwegian Research Centre and Nansen Environmental and Remote Sensing Center (NERSC))
Summary In applications of ensemble-based history matching, it is common to conduct Kalman gain or covariance localization to mitigate spurious correlations and excessive variability reduction resulting from the use of relatively small ensembles. Another alternative strategy not very well explored in reservoir applications is to apply a local analysis scheme, which consists of defining a smaller group of local model variables and observed data (observations), and perform history matching within each group individually. This work aims to demonstrate the practical advantages of a new local analysis scheme over the Kalman gain localization in a 4D seismic history-matching problem that involves big seismic data sets. In the proposed local analysis scheme, we use a correlation-based adaptive data-selection strategy to choose observations for the update of each group of local model variables. Compared to the Kalman gain localization scheme, the proposed local analysis scheme has an improved capacity in handling big models and big data sets, especially in terms of computer memory required to store relevant matrices involved in ensemble-based history-matching algorithms. In addition, we show that despite the need for a higher computational cost to perform model update per iteration step, the proposed local analysis scheme makes the ensemble-based history-matching algorithm converge faster, rendering the same level of data mismatch values at a faster pace. Meanwhile, with the same numbers of iteration steps, the ensemble-based history-matching algorithm equipped with the proposed local analysis scheme tends to yield better qualities for the estimated reservoir models than that with a Kalman gain localization scheme. As such, the proposed adaptive local analysis scheme has the potential of facilitating wider applications of ensemble-based algorithms to practical large-scale history-matching problems.
This page provides SPE members access to the April 2021 issue -- digital, pdf, and online. New! JPT Digital Edition Login required on that site; use your SPEorg login Access for those who received print JPT Digital archive of issues back to Jan 2020 is available – scroll down from the current issue cover. These are the papers synopsized in JPT this month. They are available to SPE members only through 31 May 2021. There are also links to them at the bottom of each related synopsis.
This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term "motifs" that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. The concept of rate transient analysis (RTA) involves the use of rate and pressure trends of producing wells to estimate properties such as permeability and fracture surface area.
History matching is a critical step for dynamic reservoir modeling to establish a reliable, predictive model. Numerous approaches have emerged over decades to accomplish a robust history-matched reservoir model. As geological and completion complexity of oil and gas fields increase, building a fully representative predictive reservoir model can be arduous to almost impossible. The complete paper outlines an approach to history matching that uses artificial intelligence (AI) with an artificial neural network (ANN) and data-driven analytics.
A robust, reliable work flow for well-spacing optimization in a shale reservoir development incorporating various types of uncertainties and detailed economics analysis is necessary for achieving sustainable unconventional production. In the complete paper, the authors describe a novel well-spacing-optimization work flow based on the results of assisted history matching and apply it to a real shale gas well, incorporating uncertainty parameters such as matrix permeability, matrix porosity, fracture half-length, fracture height, fracture width, fracture conductivity, and fracture water saturation. The work of well-spacing optimization is significant because it will subsequently dominate the planning of the drilling job and completion job and ultimately will affect recovery efficiency. The purpose of well-spacing optimization serves to maximize either capital revenue or ultimate recovery. The greatest challenge for well-spacing optimization is how to interpret the uncertainties associated with unconventional reservoirs.
In the words of the authors of paper SPE 202460, one of three outstanding SPE conference papers selected by reviewer Gopi Nalla of DeGolyer and MacNaughton for this month's feature, "As geological and completion complexity of oil and gas fields increases, building a fully representative predictive reservoir model can be arduous to almost impossible." However, as we see in these papers, new tools and techniques are being developed to match the ability of engineers to meet the challenges posed by assets such as shale reservoirs and maturing fields. The authors of paper SPE 200466 developed a novel method in which the results of assisted history matching are applied to well-spacing optimization in a shale development, using information extracted from uncertainty and the interpolated maximum net present value. Applied to a real shale well, it is determined by the authors to be reliable enough to apply to other wells. Paper SPE 202460 exemplifies the accelerating development and popularity of artificial intelligence (AI) in industry applications as its authors outline what they call a top-down approach that uses AI and artificial neural networks to obtain good matching results for a geologically complex offshore Malaysian field.
Choudhary, Manish Kumar (Brunei Shell Petroleum Company Sendirian Berhad) | Mahanti, Gaurav (Brunei Shell Petroleum Company Sendirian Berhad) | Rana, Yogesh (Brunei Shell Petroleum Company Sendirian Berhad) | Garimella, Sai Venkata (Brunei Shell Petroleum Company Sendirian Berhad) | Ali, Arfan (Brunei Shell Petroleum Company Sendirian Berhad) | Li, Lin (Brunei Shell Petroleum Company Sendirian Berhad)
Abstract Field X is one of largest oil fields in Brunei producing since 1970's. The field consists of a large faulted anticlinal structure of shallow marine Miocene sediments. The field has over 500 compartments and is produced under waterflood since 1980's through 400+ conduits over 50 platforms. A comprehensive review of water injection performance was attempted in 2019 to assess remaining oil and identify infill opportunities. Large uncertainties in reservoir properties, connectivity and fluid contacts required that data across multiple disciplines is integrated to identify new opportunities. It was recognized early on that integrated analysis of surveillance data and production history over 40 years will be critical for understanding field performance. Hence, reviews were first initiated using sand maps and analytical techniques. Tracer surveys, reservoir pressures, salinity measurements, Production Logging Tool (PLT) were all analyzed to understand waterflood progression and to define connectivity scenarios. A complete review of well logs, core data from over 30 wells and outcrop studies was carried out as part of modelling workflow. This understanding was used to construct a new facies-based static model. In parallel, key dynamic inputs like PVT analysis reports and special core analysis studies were analyzed to update dynamic modelling components. Prior to initiating the full field model history matching, a comprehensive impact analysis of the key dynamic uncertainties i.e., Production allocation, connectivity and varying aquifer strength etc. were conducted. An Assisted History Matching (AHM) workflow was attempted, which helped in identifying high impacting inputs which could be varied for history matching. Adjoint techniques were also used to identify other plausible geological scenarios. The integrated review helped in identifying over 50 new opportunities which potentially can increase recovery by over 10%. The new static model identified upsides in Stock Tank Oil Initially in Place (STOIIP) which if realized could further increase ultimate recoverable. The use of AHM assisted in reducing iterations and achieve multiple history matched models, which can be used to quantify forecast uncertainty. The new opportunities have helped to revitalize the mature field and has potential to almost increase the production by over 50%. A dedicated team is now maturing these opportunities. The robust methodology of integrating surveillance data with simulation modelling as described in this paper is generic and could be useful in current day brown field development practices to serve as an effective and economic manner for sustaining oil production and maximizing ultimate recovery. It is essential that all surveillance and production history data are well analyzed together prior to attempting any detailed modelling exercise. New models should then be constructed which confirm to the surveillance information and capture reservoir uncertainties. In large oil fields with long production history with allocation uncertainties, it is always a challenge for a quantitative assessment of History match quality and infill well Ultimate Recovery (UR) estimations. Hence a composite History Match Quality Indicator (HMQI) was designed with an appropriate weightage of rate, cumulative & reservoir pressure mismatch, water breakthrough timing delays. Then HMQI parameter spatial variation maps were made for different zones over the entire field for understanding and appropriately discounting each infill well oil recovery. Also, it is critical that facies variation is properly captured in models to better understand waterfront movements and locate remaining oil. Dynamic modelling of mature field with long production history can be quite challenging on its own and it is imperative that new numerical techniques are used to increase efficiency.