Field Operators Research Articles

Assessing polyacrylamide solution chemical stability during a polymer flood in the Kalamkas field, Western Kazakhstan

During a polymer flood, the field operator must be convinced that significant chemical investment is not compromised at the early stages of polymer injection. Further, dissolved oxygen in the viscous polymer solution must be controlled at a safe level, where viscosity loss will be insignificant. Under anaerobic conditions,the hydrolyzed polyacrylamide (HPAM) solution is stable even if iron ions are present in the process water. Thus, in the field operation, introduced oxygen and existing iron ions will cause an enormous viscosity decline. The geochemical calculation reveals that dissolved oxygen can rapidly deplete after entering Kalamkas formation. This paper confirms this prediction through a combination of laboratory measurements and field observations. This study is based on rheology measurements of polymer solutions and produced fluid from the offset production well associated with the Kalamkas oilfield in Western Kazakhstan. Comprehensive analysis confirms no viscosity loss at the surface facilities during polymer preparation and injection at a Pol- ymer Slicing Unit and significant viscosity loss at an Eductor-type unit caused by oxygen introduced during polymer solution preparation. However, even introduced high dissolved oxygen levels that degrade polymer at the surface can be rapidly depleted during contact with the formation, thereby promoting polymer chemical stability in the reservoir.

Ghost and tachyon free propagation up to spin 3 in Lorentz invariant field theories

We complete the set of spin-projector operators for fields up to rank-3 by providing all operators connecting sectors with same spin and parity. In this way we can broaden the search for unitary and non-tachyonic particle propagation in quadratic lagrangian with inter-field mixing. We use the properties of projector algebra to reanalyze known theories and shed light towards new healthy ones. We do so with full control over the gauge constraints by working the form of the saturated propagator in an appropriate frame of reference.

The Resolution of Euclidean Massless Field Operators of Higher Spins on $${\mathbb {R}}^6$$ and the $$L^2$$ Method

The Resolution of Euclidean Massless Field Operators of Higher Spins on $${\mathbb {R}}^6$$ and the $$L^2$$ Method

Extending the I-95 Rule-Based Incident Duration System With an Automated Knowledge Transferability Model

The rule-based incident duration prediction model (IDPM), covering Interstate highways I-95, I-495, and I-695, has been adopted by the Maryland Department of Transportation State Highway Administration in its daily responses to non-recurrent congestion. In light of its effectiveness and robustness in practice, expanding such a system to all other highways emerges as desirable but a challenging task, because of the need to integrate field operators’ expertise in generating prediction rules and the dependence on sufficient incident records for key parameter calibration. To circumvent such a data-demanding and time-consuming process for knowledge acquisition and refinement for extending the IDPM’s spatial coverage, this study has proposed a knowledge transferability analysis (KTA) method, featuring its automated process to assess, select, and transfer existing prediction rules to perform incident duration estimate for the new target highway. Evaluation of the proposed KTA with the incident records from Maryland I-70, using both transferred and customized local rules, reveals that it can achieve accuracy of 87% with the training dataset (i.e., 2016–2018) and 82% with the test dataset (i.e., 2019), comparable to the current system’s performance but demanding much fewer incident records for model calibration and significantly less effort for system expansion.

Consistent quantization of systems with positive and negative energy states

The Stueckelberg-Feynman (SF) treatment, where positive energy antiparticles are described as negative energy particles going backward in time, lies on the basis of particle physics, but it was inconsistent with quantum field theory, since led to a negative norm for negative energy states. In the paper a new consistent method of canonical quantization in SF treatment is presented, where norms of all states is positive, since changing the direction of time integration in the action function changes the sign of Lagrangian of antiparticles and momentum. Minimal Lagrangians for complex canonical variables do not lead to the zero-point energy, which partially solves the cosmological constant problem. Causal propagators and amplitudes appear as symmetric chronological products of field operators, which slightly modifies diagram technique. Modified microcausality conditions and proof of spin and statistics theorem are presented, applications to particle physics and condensed media are discussed.

Transfer Learning with Recurrent Neural Networks for Long-Term Production Forecasting in Unconventional Reservoirs

Summary Robust production forecasting allows for optimal resource recovery through efficient field management strategies. In hydraulically fractured unconventional reservoirs, the physics of fluid flow and transport processes is not well understood and the presence of and transitions between multiple flow regimes further complicate forecasting. An important goal for field operators is to obtain a fast and reliable forecast with minimal historical production data. The abundance of wells drilled in fractured tight formations and continuous data acquisition effort motivate the use of data-driven forecast methods. However, traditional data-driven forecast methods require sufficient training data from an extended period of production for any target well, which may have limited practical use when the effective production life of wells is relatively short. In this paper, a deep recurrent neural network (RNN) model is developed for long-term production forecasting in unconventional reservoirs. As input data, the model takes completion parameters, formation and fluid properties, operating controls, and early (i.e., 3–6 months) production response data. The model is trained on a collection of historical production data across multiple flow regimes, control settings, and the corresponding well properties from multiple shale plays. The proposed RNN model can predict oil, water, and gas production as multivariate time series under varying operating controls. Once the forecast model is trained, it can be used to obtain a one-step forecast by feeding the model with input well properties, operating controls, and a short initial production. The long-term forecast is obtained by either recursively feeding the model with forecast results from the preceding timesteps or by training the model for multistep ahead predictions. Unlike other applications of RNN that require a long history of production data for training, our model employs transfer learning by combining early production data from the target well with the long-term dynamics captured from historical production data in other wells. We illustrate our approach using synthetic data sets and a case study from Bakken Play in North Dakota.

Multi-solution well placement optimization using ensemble learning of surrogate models

Well location optimization aims to maximize the economic profit of oil and gas field development while respecting various constraints. The limitations of the currently available well placement optimization workflows are their 1) high computational requirements, which makes them inappropriate for full-field applications where a large number of wells have to be optimized using a computationally expensive simulation model; and 2) providing a single optimal solution, whereas on-site operational problems often add unforeseen constraints that result in adjustments to this optimal, inflexible scenario degrading its value. This study presents a multi-solution, surrogate models (SMs)-assisted optimization framework to deliver diverse, close-to-optimum well placement scenarios at a reasonable computational cost. Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used as the optimizer while diversity in optimal solutions is achieved by multiple, parallel runs of the optimizer with different starting points. Convolutional Neural Network (CNN) is used as the SM, to partly substitute the computationally expensive reservoir model runs during the optimization process. A new, adjusted Latin Hypercube Sampling (aLHS) procedure is developed to generate initial training datasets with diverse well placement scenarios while respecting reservoir boundaries and well spacing constraints. An ensemble of CNNs is pre-trained using the generated dataset to enhance the robustness of the surrogate modeling as well as to allow estimation of the SM's prediction quality for new data points. The ensemble of CNNs is adaptively updated during the optimization process using selected new data points, to improve the SM's prediction accuracy. To the best of our knowledge, this is the first application of ensemble learning strategy to a well placement optimization problem. The added value of the framework is demonstrated by comparing three optimization approaches on the Brugge and Egg field benchmark case studies. The approaches are 1) ‘no SM’: using the actual reservoir model only, 2) ‘Offline SM’: the optimization is performed using SM-only that is pre-trained using initial training datasets generated by the actual reservoir model, and 3) ‘Online SM’: pre-trained CNNs are adaptively updated during the optimization process using new datasets generated using the actual reservoir model. The surrogate-assisted optimization approach substantially reduced the computation time, while a greater objective value was achieved by employing the adaptive learning strategy due to the enhanced prediction accuracy of the SMs. Multiple diverse solutions were obtained with different well locations but close-to-optimum objective values, which allows a more efficient exploration of the search space at a significantly reduced computational cost. The presented workflow integrates critical challenges that are correlated, yet often addressed independently, providing the much-required operational flexibility and computational efficiency to field operators when selecting from the optimal well placement scenarios. • A surrogate-assisted, multi-solution framework is presented for well placement optimization. • An ensemble of CNNs is used as the surrogate model (SM) and is adaptively updated during the optimization process, to partly substitute the computationally expensive reservoir simulation runs. • An adjusted Latin Hypercube Sampling (aLHS) procedure is developed to generate initial training datasets with diverse well placement scenarios while several constraints. • The developed framework allows a more efficient exploration of the search space, providing the much-required operational flexibility to field operators when selecting from the optimal well placement scenarios.

Flow Velocity and Sand Loading Effect on Erosion–Corrosion during Liquid-Solid Impingement on Mild Steel

The presence of CO2, sand, and water in oil and gas reservoirs causes erosion–corrosion leading to material degradation in pipelines and fluid handling equipment that results in increasing maintenance and repair costs and a decrease in production. While the weight loss caused by erosion–corrosion is known to depend on flow velocity, angle of impact, sand loading and size and target material properties, field operators often limit the flow rate based on a critical corrosion velocity to protect the equipment. This study investigates the effects of sand loading and flow velocity on weight loss associated with erosion–corrosion in a mild steel sample using a submerged impingement jet. The weight loss by erosion, corrosion and their interaction for a flow velocity range of 10 m/s to 20 m/s and sand loading range of 300 mg/L to 600 mg/L, in a seawater environment, are presented. The results showed that the weight loss by pure erosion and erosion–corrosion interaction increases linearly with jet velocity and sand loading, and that erosion is dominant in all cases except at low velocity and sand loading. The scanning electron microscope (SEM) images after impingement tests were analyzed. In addition, correlations for the velocity and sand loading were derived using the design of experiment method (DOE).

Exactly solvable 2D model for photon propagation in curved space: loss of interference and Bell inequality violation

We present an exact solution for the propagation of quantized massless scalar particles in a two-dimensional variation of the Alcubierre metric. Classical localized wavepacket solutions are derived using closed expressions for light-ray coordinates, and corresponding annihilation operators are constructed using the concept of locally positive and negative frequencies. The theory is used to describe the loss of fringe visibility in a single-photon interferometer, and the reduction of entanglement between two 2D photons, if one photon travels through a region with spacetime curvature. We derive an expansion of the field operator in terms of localized modes by means of an over-completeness relation. The quantization procedure also applies to massive and charged scalar fields in an n-dimensional globally hyperbolic spacetime.

Three-dimensional nonlinear vibration model and fatigue failure mechanism of deepwater test pipe

In deepwater test condition, the riser–test pipe (tubing string) system (RTS) is subject to the vortex-induced effect on riser, flow-induced effect on test pipe and longitudinal/transverse coupling effect, which is prone to buckling deformation, fatigue fracture and friction perforation. To resolve this, the three-dimensional (3D) nonlinear vibration model of deepwater RTS is established using the micro-finite method, energy method and Hamilton variational principle. Based on the elastic–plastic contact collision theory, the nonlinear contact load calculation method between riser and test pipe is proposed. Compared with experimental measurement results, calculation results using the proposed vibration model in this study and the single tubing vibration model in our recent work, the correctness and effectiveness of the proposed vibration model of the deepwater RTS are verified. Meanwhile, the cumulative damage theory is used to establish the fatigue life prediction method of test pipe. Based on that, the influences of outflow velocity, internal flow velocity, significant wave height, as well as top tension coefficient on the fatigue life of test pipe are systematically analyzed. The results demonstrate that, first, with the increase in outflow velocity, the maximum alternating stress and the annual fatigue damage rate increased. The location where fatigue failure of the test pipe is easy to occur at the upper “one third” and the bottom of test pipe are easy to occur fatigue failure. Second, with the increase in internal flow velocity, the “one third damage effect” of the test pipe will decrease, and the “bottom damage effect” of the test pipe increased that needs the attention of field operators. Third, during field operation, it is necessary to properly configure the top tension coefficient so that there can be a certain relaxation between the riser and the test pipe, so as to cause transverse vibration and consume some axial energy and load. The study led to a theoretical method for safety evaluation and a practical approach for effectively improving the fatigue life of deepwater test pipe.

An Intelligent Color Image Recognition and Mobile Control System for Robotic Arm

The aim of this study is to develop intelligent color recognition, mobile control, and monitoring system for a pick-and-place robotic arm for manufacturing systems. The demand for smart manufacturing factories with real-time control of fabricating processes and traceability of production information is increasing urgently. Generally speaking, a smart manufacturing facility is usually composed of sensing, computing, control, and communication technologies together. In this study, the three-tier architecture of the Internet of things (IoT) was adopted as a guideline to design mobile devices to control and monitor a color image recognition and alarm monitoring system by using Raspberry Pi and a web page database. The practical results and contributions of this study are as follows: With integrating the techniques of advanced BR PLC, mobile devices and APP, color image recognition, Raspberry Pi microcomputer, and MySQL database technologies together, (1) the mobile control and monitoring system is able to supervise a real-time manufacturing plant anywhere and anytime with mobile devices easily; (2) the color identification system can identify and classify different color work-piece precisely, and the identification results are recorded for remote database platform; (3) the collected data are analyzed and displayed on mobile devices through the web database for field operators and engineers promptly. It provides a very successful practical paradigm to promote conventional factories to meet industry 4.0.

Analytic Model for Feature Maps in the Primary Visual Cortex.

A compact analytic model is proposed to describe the combined orientation preference (OP) and ocular dominance (OD) features of simple cells and their mutual constraints on the spatial layout of the combined OP-OD map in the primary visual cortex (V1). This model consists of three parts: (i) an anisotropic Laplacian (AL) operator that represents the local neural sensitivity to the orientation of visual inputs; and (ii) obtain a receptive field (RF) operator that models the anisotropic spatial projection from nearby neurons to a given V1 cell over scales of a few tenths of a millimeter and combines with the AL operator to give an overall OP operator; and (iii) a map that describes how the parameters of these operators vary approximately periodically across V1. The parameters of the proposed model maximize the neural response at a given OP with an OP tuning curve fitted to experimental results. It is found that the anisotropy of the AL operator does not significantly affect OP selectivity, which is dominated by the RF anisotropy, consistent with Hubel and Wiesel's original conclusions that orientation tuning width of V1 simple cell is inversely related to the elongation of its RF. A simplified and idealized OP-OD map is then constructed to describe the approximately periodic local OP-OD structure of V1 in a compact form. It is shown explicitly that the OP map can be approximated by retaining its dominant spatial Fourier coefficients, which are shown to suffice to reconstruct its basic spatial structure. Moreover, this representation is a suitable form to analyze observed OP maps compactly and to be used in neural field theory (NFT) for analyzing activity modulated by the OP-OD structure of V1. Application to independently simulated V1 OP structure shows that observed irregularities in the map correspond to a spread of dominant coefficients in a circle in Fourier space. In addition, there is a strong bias toward two perpendicular directions when only a small patch of local map is included. The bias is decreased as the amount of V1 included in the Fourier transform is increased.

Gravitational collapse of quantum fields and Choptuik scaling

Gravitational collapse into a black hole has been extensively studied with classical sources. We develop a new formalism to simulate quantum fields forming a black hole. By choosing a convenient coherent state, this formalism taps into well-established techniques used for classical collapse and adds on the evolution of the mode functions of the quantum field operator. Divergences are regularized with the cosmological constant and Pauli-Villars fields. Using a massless spherically symmetric scalar field as an example, we demonstrate the effectiveness of the formalism by reproducing some classical results in gravitational collapse, and identifying the difference due to the quantum effects. We also find that Choptuik scaling in critical collapse survives in the semiclassical simulation, and furthermore the quantum deviation from the classical Choptuik scaling decreases when the system approaches the critical point.

On the Equivalence of Causal Propagators of the Dirac Equation in Vacuum-Destabilising External Fields

In QED, an external electromagnetic field can be accounted for non-perturbatively by replacing the causal propagators used in Feynman diagram calculations with Green’s functions for the Dirac equation under the external field. If the external field destabilises the vacuum, then it is a difficult problem to determine which Green’s function is appropriate, and multiple approaches have been developed in the literature whose equivalence, in many cases, is not clear. In this paper, we demonstrate for a broad class of external fields that includes all that act for a finite time, that four Green’s functions used in the literature are equivalent: Schwinger’s “proper-time” propagator; the “causal propagator” used in the “Bogoliubov transformation” method based on the canonical quantization of the field operator; and two defined using analytic continuation from complexified parameters. To do so, we formulate Schwinger’s “proper-time quantum mechanics" as a Schrödinger wave mechanics, and use this to re-derive Schwinger’s expression for the propagator as a statement relating solutions of the inhomogeneous Dirac equation to those of the inhomogeneous “proper-time Dirac equation”. This is done by constructing direct integral spectral decompositions of the Hamiltonians of both equations, and deriving a form for solutions of the inhomogeneous Dirac equation in terms of these decompositions. We then show that all four propagators return solutions of the inhomogeneous Dirac equation that satisfy the same boundary condition, which under a physically reasonable assumption is sufficient to specify the solution uniquely.

Designing monitoring networks for local earthquakes

Abstract Seismic networks are essential for monitoring local earthquakes in connection to industrial activities, including wastewater injection and CO2 sequestration. Because these networks are typically deployed for short periods of time at specific sites, it is beneficial to develop best practices for efficient and effective installation and monitoring. Such standards are available for regional earthquake and microseismic monitoring, but not readily available for local scale (tens of kilometer scale) monitoring. Once the region of interest has been determined, the key parameters for establishing a network are available monitoring station locations, site noise, and site utility. Networks should be designed based on the project monitoring goals for earthquake magnitude and density. Herein, a network was established at the Patterson and Hartland Oil Fields in western Kansas in association with a US Department of Energy CarbonSAFE carbon capture and sequestration research project. We employed observations from an analog local network, the Wellington Earthquake Monitoring Network in south-central Kansas, to assess site noise and estimate earthquake detection thresholds for the Patterson and Hartland fields. Noise from oil production facilities was evaluated, concluding that oil lease sites are suitable for monitoring small local earthquakes (M1-M3). The network was designed to have a magnitude of completeness of M1 while using station locations on existing field operator leases. Fifteen months of continuous network operation demonstrates reliable and efficient local earthquake monitoring and provides best practices recommendations for similar operations.

A topology optimization algorithm for magnetic structures based on a hybrid FEM\u2013BEM method utilizing the adjoint approach

A method to optimize the topology of hard as well as soft magnetic structures is implemented using the density approach for topology optimization. The stray field computation is performed by a hybrid finite element–boundary element method. Utilizing the adjoint approach the gradients necessary to perform the optimization can be calculated very efficiently. We derive the gradients using a “first optimize then discretize” scheme. Within this scheme, the stray field operator is self-adjoint allowing to solve the adjoint equation by the same means as the stray field calculation. The capabilities of the method are showcased by optimizing the topology of hard as well as soft magnetic thin film structures and the results are verified by comparison with an analytical solution.

Rank-2 toric code in two dimensions

We study a two-dimensional spin model obtained by "Higgsing" the rank-2 U(1) lattice gauge theory (LGT) with scalar or vector charges on the L_x * L_y square lattice under the periodic boundary condition (PBC). There are p degrees of freedom per orbital and three orbitals per unit cell in the spin model. The resulting spin model is a stabilizer code consisting of three mutually commuting projectors that are, in turn, obtained by Higgsing the mutually commuting Gauss's law operators and the magnetic field operators in the underlying LGT. The spin model thus obtained is exactly solvable, with the ground state degeneracy (GSD) D given by log_p D=2+(1+delta_{L_x mod p,0})(1+delta_{L_y mod p,0}) when p is a prime number. Two types of dipole excitations, pristine and emergent, are identified. Both the monopoles and the dipoles are free to move, with restrictions on monopoles to hop only by p lattice spacing along with certain directions. The monopole-monopole braiding phase depends on the separation of the x or y coordinates of the initial monopole positions, making it distinct from the ordinary anyon braiding statistics. The monopole-dipole braiding obeys the usual anyonic statistics. Despite the oddity, the monopole-monopole braiding phase can be understood as the Aharonov-Bohm phase of some emergent vector potentials.

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Optimal spraying task assignment problem in crop protection with multi-UAV systems and its order irrelevant enumeration solution

Creating a team of crop protection unmanned aerial vehicles (UAVs) for a scheduled spraying task over fields is a method that seeks to overcome the various limitations of using individual UAVs. However, given some of the issues with pesticide spraying operations involving multiple UAVs (multi-UAVs), such as differences in hardware, irregular field borders, spray drift, and operator proficiency, assigning spraying sub-tasks to multi-UAVs during crop protection is more important and more complex than for other homogeneous multi-agent systems in common use. In this study, a mathematical model of a heterogeneous multi-UAV crop protection system is established. Based on this model, an optimal spraying task assignment problem (OSTAP) for multi-UAVs used in crop protection is proposed. To solve this problem, it is converted to an equivalent combinatorial problem. The order irrelevant enumeration solution (OIES) algorithm is proposed where time and space complexities are more efficiently implemented on a horizontal comparison among OIES, equal task assignment (ETA), sequential task assignment (STA), discrete particle swarm optimisation (DPSO), and a full permutation algorithm. Numerical simulation with a six-UAV system showed that OIES could solve OSTAP in a finite time using C++ in the Visual Studio environment and performed efficiently and accurately. Finally, with the proposed OIES, an OSTAP application software was developed as a visualisation of an OSTAP solution. With this application, a spraying test with the five-UAV system was carried out to determine the effectiveness of OIES in an actual pesticide spraying operation. Test results showed that OIES performs best with these algorithms, which takes only 51.3% of the operating time taken by ETA and it requires fewer flights.

Field Demonstration of the Impact of Fractures on Hydrolyzed Polyacrylamide Injectivity, Propagation, and Degradation

Summary During a polymer flood, the field operator must be convinced that the large chemical investment is not compromised during polymer injection. Furthermore, injectivity associated with the viscous polymer solutions must not be reduced to where fluid throughput in the reservoir and oil production rates become uneconomic. Fractures with limited length and proper orientation have been theoretically argued to dramatically increase polymer injectivity and eliminate polymer mechanical degradation. This paper confirms these predictions through a combination of calculations, laboratory measurements, and field observations (including step-rate tests, pressure transient analysis, and analysis of fluid samples flowed back from injection wells and produced from offset production wells) associated with the Kalamkas oil field in Western Kazakhstan. A novel method was developed to collect samples of fluids that were back-produced from injection wells using the natural energy of a reservoir at the wellhead. This method included a special procedure and surface-equipment scheme to protect samples from oxidative degradation. Rheological measurements of back-produced polymer solutions revealed no polymer mechanical degradation for conditions at the Kalamkas oil field. An injection well pressure falloff test and a step-rate test confirmed that polymer injection occurred above the formation parting pressure. The open fracture area was high enough to ensure low flow velocity for the polymer solution (and consequently, the mechanical stability of the polymer). Compared to other laboratory and field procedures, this new method is quick, simple, cheap, and reliable. Tests also confirmed that contact with the formation rapidly depleted dissolved oxygen from the fluids—thereby promoting polymer chemical stability.