Field Examples Research Articles

On the variational principle in the unfolded dynamics

The interplay between off-shell and on-shell unfolded systems is analyzed. The formulation of invariant constraints that put an off-shell system on shell is developed by adding new variables and derivation in the target space, that extends the original Q-derivation of the unfolded system to a bicomplex. The analogue of the Euler-Lagrange equations in the unfolded dynamics is suggested. The general class of invariant on-shell equation constraints is defined in cohomological terms. The necessary and sufficient condition for the on-shell equation constraints being Euler-Lagrange for some Lagrangian system is proven. The proposed construction is illustrated by the scalar field example.

Estimating groundwater mean transit time from SF6 in stream water: field example and planning metrics for a reach mass-balance approach

Estimating groundwater mean transit time from SF6 in stream water: field example and planning metrics for a reach mass-balance approach

Reservoir performance analysis through the material balance equation: An integrated review based on field examples

The Material Balance Equation (MBE) has proved its versatility over a century because it has been adapted to include several production mechanisms identified in homogeneous, naturally fractured, and unconventional reservoirs. Thus, numerous field cases regarding the practical use of the MBE are found in the Petroleum Engineering literature. However, those cases have been analyzed by using diverse approaches introduced at different times, leading to a wide variety of analysis methods. Several field examples are revisited in this work through an integrated review that properly uses analysis techniques focused on diagnostics, straight-line analysis, and pressure-production history matching. This review provides additional insights about the reservoir performance compared to the results presented in the original papers. Thus, the conceptual model of the reservoir-aquifer system is consistently characterized by using several graphs along the process of analysis. The reasons related to the complementary role between the conventional straight-line analysis and the history matching technique are provided; the latter should confirm the results from the straight-line analysis, while this one should guide the history matching technique. Also, based on the observation that a good history match does not guarantee the correct characterization of the reservoir-aquifer model, the relevance of integrating the Material Balance analysis with other disciplines is discussed. The significance of the multidisciplinary approach to mitigate the non-uniqueness inverse problem is illustrated through the study of complex examples, such as a naturally fractured reservoir connected to an aquifer. Furthermore, scrutiny on the concept of total effective formation-water compressibility is provided to emphasize the similarities and differences in the analysis of homogeneous and naturally fractured reservoirs through the MBE. • A comprehensive review of the material balance equation is presented through the analysis of field examples. • A standardized methodology for reservoir-aquifer characterization is applied in four field cases. • Different types of reservoirs are addressed: homogeneous and naturally-fractured oil and gas reservoirs. • The applicability of diverse unsteady state aquifer models is illustrated. • The concept of total effective formation-water compressibility is derived and examined.

Diffraction Extraction Using a Low-Rank Matrix Approximation Method

Subsurface discontinuous features such as faults, cavities, and pinch-outs are commonly related to the spatial distribution of hydrocarbon reservoir zones and safe coal mining. Diffractions generated from subsurface discontinuities carry valuable information and thus are capable of accurately revealing these geological structures. Because diffractions behave as weak amplitudes, they are easily covered by specular reflections. The low-rank method performs well for diffraction extraction from specular reflections. However, the separation results are sensitive to the noise levels in traditional rank-reduction methods. The higher the noise level, the weaker the low-rank operator; therefore, the presence of noise affects the subsequent imaging. To improve the separation quality, an improved diffraction-separation method is proposed that uses parameterized non-convex penalty functions in terms of the low-rank assumption of seismic records. This new algorithm considers arctangent penalty functions as regularization terms for an accurate low-rank matrix approximation. The proposed algorithm is used to extract diffracted energy and eliminate reflected energy from noisy data. We use applications to synthetic and field examples to demonstrate that the new algorithm is able to extract high-quality diffractions, which helps locate and reveal subsurface geological discontinuities.

Transient Electromagnetic Inversion: An ICDE-Trained Kernel Principal Component OSELM Approach

The traditional extreme learning machine (ELM) inversion of transient electromagnetic method (TEM) based on random initial weights is known to be inept for its low-computational efficiency and poor generalization performance. To solve these problems, a kernel principal component online sequential extreme learning machine (KPCOSELM) trained by an improved chaotic differential evolution (ICDE) method is proposed. An additional kernel principal component (KPC) layer is used, which reduces the dimension of TEM data and enhances the computational efficiency of online sequential extreme learning machine (OSELM). Moreover, a novel ICDE algorithm is presented for improving the learning ability and generalization performance of OSELM inversion. In the proposed ICDE, the tent chaotic sequence is adopted to enhance the global exploitation ability, and a constraint factor is added to ensure better convergence. The feasibility and effectiveness of the proposed inversion method are evaluated via four groups of experiments. The inversion results of the synthetic and field examples show that the proposed approach outperformed other methods in terms of computational efficiency and prediction accuracy, and realized satisfactory performance in TEM inversion, which provides a new strategy for the application of neural networks in TEM inversion.

Productivity model of shale gas fractured horizontal well considering complex fracture morphology

Shale gas reservoir is characterized by complex pore structure, ultra-low permeability and a large number of natural fractures. Therefore, it is necessary to apply horizontal well drilling with hydraulic fracturing to stimulate the shale gas reservoirs by forming complex fractures network composed of main fractures (i.e., hydraulic fractures) and branch fractures (i.e., activated natural fractures). During shale gas fracturing, hydraulic fractures will initiate simultaneously, deflect under the stress interference with each other, activate adjacent natural fractures and connect with them. These complex behaviors of fractures propagation would greatly complicate the production prediction after shale gas fracturing. Currently, the majority of the production prediction models neglect the complexity of fractures network. Therefore, based on Fick's diffusion law, Langmuir isothermal adsorption equation and dual-medium seepage theory, this paper derived a productivity prediction model by considering complexity of fractures network, stress-sensitive effect and ad-desorption by using point source method, Duhamel principle and Laplace transform, and solved the model with perturbation theory, discrete superposition and Stehfest numerical inversion. Then, the production prediction model was verified by a field example analysis, and the influence of the morphology of main fractures and branch fractures on productivity is explored. It indicates that the main fracture shape has a great influence on the production, and the trapezoidal mode is closer to the production practice. The large deflection of main fractures, the high conductivity and large approaching angle of branch fractures are beneficial to improving the production of fractured shale gas horizontal wells.

An insight to formation damage evolution

Formation damage can significantly defer hydrocarbon production. Formation damage is one of the key parameters used to evaluate the quality of various operations during the life of a well. The evolution of formation damage over time is rarely studied. This paper presents the pressure transient analysis (PTA) used to derive a correlation between skin factor increase and the length of time for which a well was left exposed to drilling fluid. Multiple field examples are analyzed and modeled comparatively to evaluate the magnitude of the time-lapse formation damage. A quantitative relationship between the increase in mechanical skin as a function of exposure to drilling mud is constructed by a polynomial fitting. This gives us an insight on the how the well skin increases with exposure to drilling mud. Several mathematical models have been developed to model formation damage mechanisms. However, it is cumbersome to isolate and compute the individual mechanisms contributing to observed damage. Formation damage results from a combination of the pore size distribution, fines migration, fluid compatibility problems, wettability, presence of inorganic salts deposition, insufficient well cleanup, organic substance deposition, multiphase flow effects, etc. The proposed approach uses the skin factor derived from pressure transient analysis (PTA) to track the time-lapse evolution of formation damage during exposure time to mud, from drilling to production. The methodology has been applied to various reservoir rocks to quantify the impact for a specific rock type. Results from pressure transient tests conducted immediately after drilling, casing, and perforating are compared with production tests performed multiple years after drilling confirm that there is a mathematical relation between the formation damage, type of drilling and completion fluid, formation rock type, and exposure time to such fluids. The formation damage increase is more severe for longer duration of exposure, and also more severe for higher formation permeability.

Environmental Impacts of chemical composition Produced Water (оn the Siyazan field example)

This article describes the increased environmental impact of fuel energy complex depending on growth in oil and gas production. At the moment, due to the intensification of oil and gas production, associated mineralized reservoir water is one of the main sources for environmental pollution, and sufficiently plays role at soil cover degradation. This paper reviews recent research analysis of the physical chemical composition of formation water and the effect of this composition on soil ecosystems on the example of monocline of Siyazan field. Water samples have been taken from of monocline of Siyazan field. This information has been used to determine the most significant contaminants and their geochemical behaviour. Based on the analysis results, the examined formation water samples were characterized in terms of various classifications used in oil and gas hydrogeology. And, conclusions were drawn and it was noted that the hydrochemical composition of mineralized reservoir waters to degradation of biocenoses and soil cover.

Seismic Data Regularization on Nonequispaced Grid via a Joint Sparsity-Promotion Method

The seismic data regularization problem is vital to seismic data processing. We propose a joint sparsity-promotion method based on the compressive sensing theory named the curvelet-data-driven-tight-frame-based sparsity-promoting (CDSP) method. The CDSP method regularizes the seismic data directly on the nonequispaced grid along the spatial dimension. The joint sparsity is exploited in the curvelet and data-driven tight frame transform simultaneously on the projected regular spatial grid. The projection from the nonequispaced grid to the equispaced grid is achieved by the nonequispaced discretized Fourier transform. Comparing with the curvelet-sparsity-promotion-based (CSP) regularization method, CDSP combines the advantage of the predefined curvelet transform and adaptive-learned sparse transform into one single optimization model. An alternative directional method of multipliers (ADMM) is applied to solve the optimization problem. One synthetic and two field examples show that the CDSP method performs better on the preservation of the continuity of events and produces less artifacts than the CSP method.

Incorporating Structural Constraint Into the Machine Learning High-Resolution Seismic Reconstruction

Seismic high-resolution (HR) reconstruction is a crucial process for identifying increasingly thin layers from observed seismic data. Nowadays, machine learning (ML) has been adopted in seismic resolution improvement; however, most of the ML-based methods directly use 1-D neural networks and ignore the spatial information along seismic traces. Thus, these methods cause poor stability and accuracy issues in improving the resolution of multidimensional seismic data. In this article, we propose to incorporate a structural constraint into a neural network framework to perform seismic HR reconstruction. The loss function of our network consists of two parts. One part is used to extract useful HR seismic trace features from the training set generated by the well-log data so that the network can facilitate the solution from low resolution (LR) to HR. More importantly, the other part is used to preserve the reflection structure features of seismic data, which guarantees the stability and accuracy of HR reconstruction results. Tests on synthetic and field examples verify that the proposed method can provide a better reconstruction result in terms of resisting noise, retrieving thin layers, and preserving lateral structure than the traditional method and the ML-based method with the same network framework.

Improved W-Transform Incorporating Fast Matching Pursuit Decomposition

Time-frequency analysis method is an effective tool to describe the relationship between the time and frequency of seismic signal. Accurate time-frequency results help to better delineate subsurface geological structures. S-transform (ST), due to its high resolution, is known as a kind of pragmatic time-frequency analysis tool in seismic signal processing. However, the center of the energy group estimated by the ST is shifted to the higher frequency. A recently proposed W-transform (WT), which brings in the dominant frequency, solves this problem to some extent. However, the dominant frequency calculated by the temporal average of the instantaneous frequency is unstable, which shows negative value sometimes. Therefore, an improved WT incorporating fast matching pursuit (WT-FMP) decomposition method is proposed to estimate the well-concentrated time-frequency distribution. It decomposes the seismic signal into a series of matched wavelets selected from an over-complete dictionary initially. Furthermore, the whole time-frequency distribution of the signal is generated by superposition of each wavelet’s WT coefficients. The proposed method helps to avoid the dependence on the instantaneous frequency and it owns higher resolution because the time-frequency map for signal is well localized using fast matching pursuit (FMP). Synthetic and field examples are utilized to validate the effectiveness of the proposed WT-FMP method. The results demonstrate its priority in providing more concentrated spectrum and revealing abundant stratigraphic and geological information.

Using Wavelet Packet Denoising and a Regularized ELM Algorithm Based on the LOO Approach for Transient Electromagnetic Inversion

Transient electromagnetic method (TEM) inversion is a complex nonlinear problem with high dimensionality and ill-posedness. Using traditional neural networks based on a gradient algorithm to solve the TEM inversion problem might result in a slow convergence where the model falls easily into local minima. To solve these problems, a wavelet packet denoising (WPD) technology and a regularized extreme learning machine (ELM) algorithm based on the leave-one-out cross-validation (LOO) approach are proposed in this article. First, the WPD method is based on the hard threshold, the Shannon entropy with a Sym15 wavelet, which is provided to suppress the noise from the TEM data. Moreover, the learning process of the ELM model is optimized by randomly setting the hidden layer parameters instead of updating based on the gradient algorithm, which accelerates the learning speed. Finally, the LOO methodology is introduced to optimize the regularization factor of the ELM to improve the prediction accuracy and generalization ability of the approach. The inversion results of two typical TEM layered geoelectric models, two anomalous body models, and one field example are provided to prove the validity and feasibility of the proposed approach. In addition, compared with other traditional methods [ELM, backpropagation (BP), radial basis function (RBF), and linear support vector machine (LSVM)], the introduced method achieves higher inversion accuracy, better stability, and stronger forward data fitting ability, enabling it to effectively solve the TEM inversion problem.

SaltISCG: Interactive Salt Segmentation Method Based on CNN and Graph Cut

Salt body extraction plays an important role in the analysis of salt structures and the exploration of oil and gas. Seismic attributes and edge detection algorithms, which require manual effort, are the conventional methods of extracting salt boundaries from seismic images. Convolutional neural networks (CNNs) have become the state-of-the-art automatic segmentation method for seismic interpretation. However, the fully automatic results of the extraction of salt boundaries may still need to be modified to become accurate and robust enough for practical production. We present a novel deep-learning-based interactive segmentation method for extracting salt boundaries. To incorporate the interaction points into our method, we transform positive and negative points into two Euclidean distance maps (EDMs), which are combined with seismic images to train our CNN model. The model is composed of a U-net and a pyramid pooling module, and it is trained on the TGS Salt Identification Challenge dataset. Then, we use a graph cut algorithm to refine the likelihood maps predicted by our CNN model and subsequently update the salt boundaries. Some field examples show that the proposed method outperforms fully automatic CNN methods with a higher matching degree of the ground truth.

Denoising Deep Learning Network Based on Singular Spectrum Analysis—DAS Seismic Data Denoising With Multichannel SVDDCNN

Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious advantages over geophones, some weaknesses may limit its application. The main challenge is that DAS is polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. To suppress these novel noises, we developed a new denoising neural network based on singular spectrum analysis—multichannel singular value decomposition denoising convolutional neural network (SVDDCNN). The network can simultaneously extract data features from singular spectrum instead of the time domain, which can represent geophysical features more accurately and help separate signals from noises. Second, a multichannel input layer is designed, and the input is decomposed into three subspaces by singular spectrum analysis, which provides records of different signal-to-noise ratios (SNRs) for training and improves generalization ability of the network. Third, to enhance the quality of the data set, we added the noise subspace records removed by SVD into the training set to provide various forms of noise with different singular spectra. Both synthetic and field examples show that our network has achieved impressive denoising of DAS VSP and demonstrated competitive performance compared with other methods. Furthermore, the structure similarity (SSIM) map is introduced to evaluate the signal leakage by calculating the similarity between the denoised record and the removed noise record. The lowest SSIM index of the proposed network indicated superior signal preservation ability.

Static-shift suppression and anti-interference signal processing for CSAMT based on Guided Image Filtering

Shallow conductive heterogeneity can lead to static shifts ain the apparent resistivity sounding curve of controlled-source audio-frequency magnetotellurics (CSAMT). The static effect will shift the apparent resistivity curves along with axial log-log coordinates. Such an effect, if not properly processed, can distort the resistivity of rock formation and the depth of interfaces, and even make the geological structures unrecognizable. In this paper, we discuss the reasons and characteristics of the static shift and summarize the previous studies regarding static shift correction. Then, we propose the Guided Image Filtering algorithm to suppress static shifts in CSAMT. In detail, we use the multi-window superposition method to superimpose 1D signals into a 2D matrix image, which is subsequently processed with Guided Image Filtering. In the synthetic model study and field examples, the Guided Image Filtering algorithm has effectively corrected and suppressed static shifts, and finally improved the precision of data interpretation.

Study on a Gas Plunger Lift Model for Shale Gas Wells and Its Effective Application

The problem of efficient gas lift for gas well annulus packers that rely on their own energy plungers is considered. The complex related gas-liquid problem is addressed in the frame of model where the gas inflow dynamics and liquid inflow dynamics of the considered shale gas wells are weakly coupled. On this basis, and with the aiding support of indoor simulation experimental data, a new gas plunger lift design taking into account liquid leakage is obtained. Finally, a dedicated software relying on this approach is developed and used to verify the reliability of the model by means of field examples.

Structural strength evaluation of injection production string in underground gas storage

In this paper, the mechanical analysis of multi cycle cyclic alternating load of injection production string is carried out. The theoretical analysis is carried out from two aspects: the analysis of pipe string pressure field and the analysis of pipe string temperature field during injection production. The effectiveness of the theory is verified by field examples.

Wear prevention & control as a preventive maintenance strategy

The engineering systems for iron and steel production in an integrated steel company are composed of multitudes of complex machineries and components for handling and transfer of large volume of aggressive raw material and intermediate products. This leads to abrasion, wear and other service induced degradation of components and machineries resulting in huge losses owing to decreased component service life, production losses, energy losses and increased maintenance cost. This inevitably requires systematic approach towards a productive maintenance strategy in order to prevent frequent failures of machineries and components adding to the operating costs and consuming more resources. Wear prevention and control through judicious selection and use of superior materials for machineries components have received greater attentions in recent times as a solution to the maintenance problems. The present paper highlights various types of wear encountered in steel processing industries, the metallurgical factors influencing wear and the strategy/ techniques to prevent it. This also includes field examples of such an attempt towards selection guidelines and their uses for wear prone components. The successful applications of some of the wear resistant materials, such as, high-Cr iron alloys, tool and alloy steels, etc.; for some of the wear prone components, such as, protective liners for material handling systems for coke and sinter, knives for shearing and chopping applications for steel strips and sheets, steel mill rolls, etc; have been enumerated.

Evaluation and Optimization Design for Microclimate Comfort of Traditional Village Squares Based on Extension Correlation Function.

The evaluation approaches for microclimate comfort in traditional villages often ignore the year-round impact and the impact from dynamic and static behaviors, as well as the composite impact of wind and heat environments. To solve the problem, this study presents an evaluation and optimization design strategy for microclimate comfort of traditional village squares based on extension correlation function, using field survey, computer simulation, and example analysis. Firstly, the wind and heat environments in the space of the square were measured on the site with an ultrasonic integrated weather station. Secondly, simulation parameters were configured on PHOENICS (computational fluid dynamics software), namely, boundary conditions, wind environment, heat environment, and green plants, and used to simulate the wind and heat environments in the space of the square, followed by a correlation analysis between the simulation results and the measured results. Finally, the extension correlation function was adopted to comprehensively evaluate the microclimate comfort of the preliminary design scheme, and the design scheme of the square was finalized through repeated adjustments. The proposed strategy was verified on an example: Xieduqi square, Zoumatang village, eastern China's Zhejiang Province. The example analysis shows that the proposed strategy is highly operable. The research effectively improves the optimization design of traditional village squares, extends the digital technology system of traditional villages, and greatly drives rural construction in the future.

First attempt to model numerically seismically-induced soft-sediment deformation structures – a comparison with field examples

No numerical model has thus far addressed seismites, even though seismites are frequently used for the reconstruction of seismic events in the geological past. This is the more remarkable since the boundary conditions which have to be fulfilled for the development of seismites have also been estimated only empirically. The present contribution is a first attempt to model numerically the soft-sediment deformation structures caused by the passage of S-waves through near-surface sedimentary layers. The simulations are based on the so-called pressure tube model and the iSALE2D program. We modelled a seismic S-wave with six different vertical velocities, ranging from 1.6 to 2.6 m · s -1 , passing through sediments with different densities and porosities in a sedimentary succession from the surface down to a depth of 10 m. The modelled soft-sediment deformation structures (load casts, flame structures, injection structures and sedimentary volcanoes) show similar geometries and sizes as those known from laboratory experiments and field studies. The geometry, size and type of these structures depend on the sediment properties and on the initial pressure used as a trigger mechanism, rather than on S-wave velocity. In contrast, the depth of the seismites appears to depend strongly on the S-wave velocity.