Numerical Reservoir Simulation Research Articles

Characterization methods for mass transfer of multiple media in shale gas reservoirs

Shale gas reservoirs are abound in multi-scale pore media and have strong heterogeneity. Gas migration mechanism in different scale pores is complex. Understanding the applicability of mass transfer characterization method for multi-media fluid is of great significance to develop numerical simulation of shale gas reservoirs. On the basis of multi-scale media distribution characteristics and the gas migration characteristics in multi-scale media of shale gas reservoirs, this paper establish the discrete media REV model, transient-cross-flow dual-media REV model, and pseudo-state-cross-flow dual-media REV model for microcells in different regions of reservoirs. The flow behavior of the microcells calculated by the above three models is compared with reservoir conditions and fracture parameters. The suitable rang of different mass transfer characterization methods for multiple media is proposed. The results showed that the cross-flow mode have a great effect on mass transfer on the early period. The greater the difference of dual media, the bigger the difference of mass transfer calculated by different cross-flow mode, the longer the duration of the difference of mass transfer. The transient-cross-flow dual-media model, and pseudo-state-cross-flow dual-media model can both accurately describe the mass transfer between porous kerogen and inorganic matrix. Mass transfer between inorganic matrix and secondary fractures should be described by discrete media model. The discrete fracture network coupled porous kerogen-inorganic matrix dual medium model can be more accurately for description of gas migration in shale gas reservoirs with stimulated reservoir volume. The model has important theoretical significance for developing numerical simulator for shale gas reservoirs.

Building a 3D faulted a priori model for stratigraphic inversion: Illustration of a new methodology applied on a North Sea field case study

Accurate reservoir characterization is needed all along the development of an oil and gas field study. It helps building 3D numerical reservoir simulation models for estimating the original oil and gas volumes in place and for simulating fluid flow behaviors. At a later stage of the field development, reservoir characterization can also help deciding which recovery techniques need to be used for fluids extraction.In complex media, such as faulted reservoirs, flow behavior predictions within volumes close to faults can be a very challenging issue. During the development plan, it is necessary to determine which types of communication exist between faults or which potential barriers exist for fluid flows. The solving of these issues rests on accurate fault characterization. In most cases, faults are not preserved along reservoir characterization workflows. The memory of the interpreted faults from seismic is not kept during seismic inversion and further interpretation of the result. The goal of our study is at first to integrate a 3D fault network as a priori information into a model-based stratigraphic inversion procedure. Secondly, we apply our methodology on a well-known oil and gas case study over a typical North Sea field (UK Northern North Sea) in order to demonstrate its added value for determining reservoir properties. More precisely, the a priori model is composed of several geological units populated by physical attributes, they are extrapolated from well log data following the deposition mode, but usually a priori model building methods respect neither the 3D fault geometry nor the stratification dips on the fault sides. We address this difficulty by applying an efficient flattening method for each stratigraphic unit in our workflow. Even before seismic inversion, the obtained stratigraphic model has been directly used to model synthetic seismic on our case study. Comparisons between synthetic seismic obtained from our 3D fault network model give much lower residuals than with a “basic” stratigraphic model. Finally, we apply our model-based inversion considering both faulted and non-faulted a priori models. By comparing the rock impedances results obtain in the two cases, we can see a better delineation of the Brent-reservoir compartments by using the 3D faulted a priori model built with our method.

Stochastic porous media modeling and high-resolution schemes for numerical simulation of subsurface immiscible fluid flow transport

This paper proposes stochastic petroleum porous media modeling for immiscible fluid flow simulation using Dykstra–Parson coefficient (VDP) and autocorrelation lengths to generate 2D stochastic permeability values which were also used to generate porosity fields through a linear interpolation technique based on Carman–Kozeny equation. The proposed method of permeability field generation in this study was compared to turning bands method (TBM) and uniform sampling randomization method (USRM). On the other hand, many studies have also reported that, upstream mobility weighting schemes, commonly used in conventional numerical reservoir simulators do not accurately capture immiscible displacement shocks and discontinuities through stochastically generated porous media. This can be attributed to high level of numerical smearing in first-order schemes, oftentimes misinterpreted as subsurface geological features. Therefore, this work employs high-resolution schemes of SUPERBEE flux limiter, weighted essentially non-oscillatory scheme (WENO), and monotone upstream-centered schemes for conservation laws (MUSCL) to accurately capture immiscible fluid flow transport in stochastic porous media. The high-order schemes results match well with Buckley Leverett (BL) analytical solution without any non-oscillatory solutions. The governing fluid flow equations were solved numerically using simultaneous solution (SS) technique, sequential solution (SEQ) technique and iterative implicit pressure and explicit saturation (IMPES) technique which produce acceptable numerical stability and convergence rate. A comparative and numerical examples study of flow transport through the proposed method, TBM and USRM permeability fields revealed detailed subsurface instabilities with their corresponding ultimate recovery factors. Also, the impact of autocorrelation lengths on immiscible fluid flow transport were analyzed and quantified. A finite number of lines used in the TBM resulted into visual artifact banding phenomenon unlike the proposed method and USRM. In all, the proposed permeability and porosity fields generation coupled with the numerical simulator developed will aid in developing efficient mobility control schemes to improve on poor volumetric sweep efficiency in porous media.

Extension of Dykstra-Parsons Model of Stratified-Reservoir Waterflood To Include Advanced Well Completions

Summary Large volumes of oil are being produced by waterflooding heterogeneous reservoirs. Careful flood-pattern design, well placement, and control are required to maximize oil recovery by delaying water breakthrough and optimizing sweep efficiency. Models that analyze the waterflood's performance and predict the production forecast, such as the Dykstra-Parsons (DP) method, are routinely used for this purpose. The DP method estimates the vertical-flooding efficiency between conventional wells producing from noncommunicating layers. This method and its various modifications have had a significant impact on the development of the theoretical description of the waterflooding process. The DP method is still routinely used for waterflood-performance prediction and analysis, flood-pattern selection, and recovery-factor calculation. Advanced well completions (AWCs) control the fluid flow at the reservoir sandface. They have become a proven, widely used technology (particularly in waterflooded reservoirs) for modifying a production or injection well's inflow/outflow rate profile along the well. In addition to this, new AWC designs that react to water breakthrough have recently become available. Incorporating a description of the AWC performance into the waterflood-analysis models will allow fast forecasting of the production profile and oil recovery, as well as help in optimizing the AWC configuration and control at the well-design stage. This work extends the DP method for rapid prediction of the waterflood's performance to AWC wells. It provides a simple means of estimating the additional, long-term value derived by controlling zonal flow rate using AWCs or any other means (e.g., well workover). The accuracy of the extended DP method's prediction has been verified by comparison against the results from a numerical reservoir simulator. Several examples illustrate the extended DP model's performance and value. The method's limitations and possible future extensions are also discussed. The presented model is a simple, transparent approach to evaluating the impact on the waterflood's oil recovery efficiency (RE) of various well-completion and control options. It can be implemented as an analytical model or as a fast simulator. This model is also the missing link between the various AWC design methods available today and the AWCs' long-term value evaluation.

Optimization of hydrocarbon water alternating gas in the Norne field: Application of evolutionary algorithms

Water alternating gas (WAG) is an enhanced oil recovery (EOR) method integrating the improved macroscopic sweep of water flooding with the increased microscopic displacement of gas injection. The optimal design of the WAG operating parameters is usually based on numerical reservoir simulation via trial and error. In this study, robust evolutionary algorithms are utilized to automatically optimize hydrocarbon WAG performance in the E-segment of the Norne field. Net present value (NPV) and two global semi-random search strategies, a genetic algorithm (GA) and particle swarm optimization (PSO), are used to optimize over an increasing number of operating parameters. The operating parameters include water and gas injection rates, bottom-hole pressures of the oil production wells, cycle ratio, cycle time, the composition of the injected hydrocarbon gas and the total WAG period. In progressive case studies, the number of decision-making variables is increased, increasing the problem complexity while potentially improving the efficacy of the WAG process. We also optimize the incremental recovery factor (IRF) within a fixed total WAG simulation time. The distinctions between the WAG parameters found by optimizing NPV and oil recovery are highlighted. This is the first known work to optimize over such a wide set of WAG variables and the first use of PSO to optimize a WAG project at the field scale. Compared to the reference cases, the best overall values of the objective functions found by GA and PSO were 13.8% and 14.2% higher, respectively, if NPV is optimized over all the above WAG operating variables, and 14.2% and 16.2% higher, respectively, if the IRF is optimized.

Application of artificial neural networks for calibration of a reservoir model

In the oil and gas industry, numerical reservoir modelling and simulation is the most common and widely used tool for decision making. However, construction of such model requires a large amount of data from sources such as well logs, seismic, core analysis and well testing. The data obtained are n ot error free, they are prone to measurement error. Moreover, due to the humongous size of reservoirs, their properties are inferred from measurements taken at only some limited locations within the vicinity. Therefore, calibration of numerical reservoir models is necessary. In this paper, artificial neural network modelling is applied for calibration of a numerical reservoir model under primary recovery. A benchmark reservoir model, referred to as PUNQ-S3, was selected to investigate the application of neural network model. The neural network model was developed by using an inverse solution method to formulate the training and testing data. From the numerical reservoir simulation point of view reservoir properties to be calibrated are the inputs while pressure and production profiles are outputs. On the contrary, we have employed the inverse. This allowed carrying out history matching without the requirement of an objective function and optimization algorithm. In order to calibrate the numerical reservoir model, the trained neural network was simulated by using historical data. The results of this study have brought forward an improved technique for history matching (calibration) of a numerical reservoir model.

Well-Placement Timing, Conductivity Loss Affect Production in Multiple-Fracture Wells

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 180448, “Effects of Well-Placement Timing and Conductivity Loss on Hydrocarbon Production in Multiple-Hydraulic-Fracture Horizontal Wells in a Liquids-Rich Shale Play,” by Shameem Siddiqui, SPE, Doug Walser, SPE, and Ron Dusterhoft, SPE, Halliburton, prepared for the 2016 SPE Western Regional Meeting, Anchorage, 23–26 May. The paper has not been peer reviewed. Horizontal wells in liquids-rich shale plays are now being drilled such that lateral and vertical distances between adjacent wells are significantly reduced. In multistacked reservoirs, fracture height and orientation from geomechanical effects coupled with natural fractures create additional complications; therefore, predicting well performance using numerical simulation becomes challenging. This paper describes numerical-simulation results from a three-well pad in a stacked liquids-rich reservoir (containing gas condensates) to understand the interaction between wells and production behavior. Numerical Simulation The reservoir simulator used for this study was designed to handle unstructured-grid-based simulation cases. Most of the numerical reservoir simulators that are used for modeling horizontal wells with multiple hydraulic fractures are based on structured grid cells in which the hydraulic fractures are modeled as symmetric biwing fractures perpendicular to the wellbore. In most cases, they use local grid refinement (LGR) to incorporate the hydraulic fractures into the model, which generally works well if a single well is involved and the grids are not tied to the Earth models. However, if the wells and the hydraulic fractures are not orthogonal to the Earth-model grids or if the reservoir contains nonorthogonal secondary fractures (natural or induced), modeling them with LGR becomes a challenge. When multiple wells that are not parallel are drilled from a single pad, properly representing the wells with hydraulic fractures in a structured-grid-based reservoir-simulation model becomes even more challenging. By contrast, the unstructured-grid-based reservoir models are not restricted by any of these limitations—they can have any geometry, size, or orientation for the wells and can include primary hydraulic fractures, secondary fractures, and open natural fractures. Instead of using grid cells that are parallel in shape, the unstructured grid cells can have any arbitrary shape. Incorporating realistic induced hydraulic fractures in a reservoir model is easier if unstructured grids are used. Reservoir-Simulation Model The reservoir model consisted of five horizontal layers with varying properties in each layer. The horizontal sections of Wells 1 and 3 go through the middle of Layer 2, and that of Well 2 goes through the middle of Layer 4. Each well is completed exactly the same way.

A Semianalytical Method for Modeling Two-Phase Flow in Coalbed-Methane Reservoirs With Complex Fracture Networks

Summary Coalbed-methane (CBM) reservoirs are naturally fractured formations with cleats surrounding the coal matrix. Analyzing and predicting CBM-production performance is challenging, especially for early-time production, because of the complex fracture networks and gas/water two-phase flow. In this study, we develop an efficient semianalytical model to predict gas and water production in CBM reservoirs with multiscale fracture networks. The activated large-scale or interconnected cleats and hydraulic fractures are modeled explicitly as discretized segments with connected nodes. The small-scale cleats and disconnected natural fractures are described implicitly as “enhanced matrix permeability.” We incorporate critical gas-flow mechanisms and stress sensitivity of the fracture network in the model. The two-phase-flow mechanism is considered by iteratively correcting the relative permeability to gas/water for each fracture segment and capillary pressure at each node with the reservoir depletion. We verified the model against a numerical reservoir simulator, field data, and an analytical solution. Subsequently, we apply the model to quantify the effects of fracture-network complexity/connectivity and stress sensitivity on gas/water-production behavior. This work presents an accurate and fast semianalytical model to perform two-phase flow of gas and water in CBM wells with complex fracture networks. The approach is easier to set up and less data-intensive than using a numerical reservoir simulator, and more flexible in handling the complex-fracture networks than full analytical models. This method provides a promising technique for better understanding the effect of the cleats and fracture networks present in CBM reservoirs on gas and water production.

A model for pseudo-steady and non-equilibrium sorption in coalbed methane reservoir simulation and its application

Coalbed methane (CBM) reservoir simulation is vital in CBM development. Production of CBM is controlled by a three-step process: gas desorption from the coal matrix, gas diffusion to the cleat system, and gas flow through fractures. This process follows the Langmuir's law, Fick's first law and Darcy's law. In this work, we suggested a three-dimensional, dual-porosity, two-phase, pseudo-steady, non-equilibrium sorption mathematical model for overall progresses from desorption to flow by the help of petroleum reservoir numerical simulation method and this complex mathematical model is approximated and solved by finite-difference and fully implicit method. The CBM reservoir simulation software, CBMRS 1.0 in Korean interface was developed by C++. It was compared with COMET 3D and evaluated and it could be exclusive software for CBM reservoir simulation.

Water-out performance and pattern of horizontal wells for marine sandstone reservoirs in Tarim Basin, NW China

Abstract Based on geological analysis, reservoir numerical simulation and production performance analysis, water-out performance and pattern of horizontal wells in Tarim marine sandstone reservoir were studied. Compared with continental sandstone reservoirs, the marine sandstone reservoirs in Tarim Basin were characterized by low oil viscosity, good reservoir continuity, and development of interbeds, which together with the large amount of horizontal wells, resulted in fast production rate and high recovery degree of the reservoirs. The main controlling factors of uneven water-out in horizontal wells were reservoir seepage barrier, injection-production well pattern, and dominant seepage channel. Thus 9 types in 4 categories of typical water-out pattern of horizontal wells in Tarim marine sandstone reservoirs were identified, and water-out management measures were proposed for them respectively according to their water-out mechanism and remaining oil distribution characteristics. Finally, the water-out pattern can be identified based on the inflection characteristics of derivative curve of water-oil ratio. This study of the water-out pattern can provide guidance for the adjustment policy of water injection in horizontal wells in marine sandstone reservoirs of Tarim Oilfield.

Fast evaluation of well placements in heterogeneous reservoir models using machine learning

Surrogate models, or proxies, provide computationally inexpensive alternatives for approximating reservoir responses. Proxy models are routinely developed to generate spatially-varying output such as field pressures and saturations, or well responses such as production rates and bottom-hole pressures. In this study, a machine learning approach is adopted to predict reservoir responses based on injector well locations. The proxy developed in this work is trained to reproduce reservoir-wide objective functions, i.e., total profit, cumulative oil/gas produced, or net CO2 stored.Because of the geological complexity of most reservoirs, slight adjustments in injector well locations could yield dramatic changes in the objective function responses. Hence, most proxies do not include well locations as inputs in their formulation. This complex relationship between well locations and reservoir-wide responses makes non-parametric, machine learning-based methods an attractive option. We introduce a machine learning approach in which the primary predictors are physical well locations, and the primary response is a defined objective function such as NPV. The complexity of the response surface with respect to well locations necessitates that we augment the predictor variables with well-to-well pairwise connectivities, injector block permeabilities and porosities, and initial injector block saturations. Introducing well-to-well connectivities yields significant improvements in prediction accuracy. Connectivities are represented by ‘diffusive times of flight’ of the pressure front, which is computed using the Fast Marching Method.A handful of training observations are obtained from numerical reservoir simulations. The Extreme Gradient Boosting method is then used to build an intelligent model for making predictions given any set of observations. The proposed approach is demonstrated using five synthetic case studies: i) a homogeneous reservoir waterflood, ii) a channelized reservoir waterflood, iii) a 20-model ensemble waterflood, iv) a CO2 flood in a heterogeneous reservoir, v) a CO2 flood in a heterogeneous reservoir with complex topography. Results show a significant correlation between proxy predictions and reservoir simulation results.

Time-dependent shape factors for fractured reservoir simulation: Effect of stress sensitivity in matrix system

Matrix-fracture transfer functions are the backbone of any dual-porosity or dual-permeability formulation in the modeling of fluid flow in fractured porous media. The chief feature within them is the accurate definition of shape factors. To date, there is no commonly accepted formulation of a matrix-fracture transfer function and shape factor. Many unsteady transfer formulations of shape factors for incompressible reservoirs have been presented and used. However, the assumption of incompressible reservoirs for these formulations is not consistent with the in-situ reservoir conditions, and a few have been reported on the application of time-dependent shape factor on dual-porosity numerical reservoir simulation. The focus of this study is, therefore, to find the time-dependent shape factor for the single-phase flow of stress-sensitive reservoirs. In this paper, the stress sensitive curves and corresponding fitting equations of a fractured reservoir are obtained based on the laboratory physical simulation; and, a model for the determination of the time-dependent shape factors that considers the stress sensitivity is presented; then the solution of nonlinear pressure diffusivity equation is used to derive the shape factor; finally, the shape factor obtained in this paper is segmented by time and applied to the dual-porosity numerical reservoir simulation. The Boltzmann transform, the separation variable method and the method of power series are used to solve the nonlinear governing equation by considering the stress sensitivity in matrix system. The approximate analytical model for the shape factor presented in this paper is verified using fine-grid, finite element numerical simulation. The dependency of the shape factor on the matrix stress sensitivity is also investigated. The theoretical analysis presented improved our understanding of transfer flow in fractured porous media, and provides a new method for dual-porosity numerical reservoir simulation of fractured reservoirs.

Simplification workflow for hydraulically fractured reservoirs

Abstract Production from unconventional formations, such as shales, has significantly increased in recent years by stimulating large portions of a reservoir through the application of horizontal drilling and hydraulic fracturing. Although oil shales are heavily dependent on oil prices, production forecasts remain positive in the North-American region. Due to the complexity of hydraulically fractured tight formations, reservoir numerical simulation has become the standard tool to assess and predict production performance from these unconventional resources. Many of these unconventional fields are immense, consisting of multistage and multiwell projects, which results in impractical simulation run times. Hence, simplification of large-scale simulation models is now common both in the industry and academia. Typical simplified models such as the “single fracture” approach do not often capture the physics of large-scale projects which results in inaccurate results. In this paper we present a simple, yet rigorous workflow that generates simplified representative models in order to achieve low simulation run times while capturing physical phenomena which is fundamental for accurate calculations. The proposed workflow is based on consideration of representative portions of a large-scale model followed by post-process scaling to obtain desired full model results. The simplified models that result from the application of the proposed workflow for a single well and a multiwell case are compared to full-scale models and the “single fracture” model. Comparison of fluid rates and cumulative production show that accurate results are possible for simplified models if all important components for a particular case are taken into account. Finally, application of the workflow is shown for a heterogeneous field case where prediction studies can be carried out.

Three-dimensional and two-phase numerical simulation of fractured dry gas reservoirs

A significant percentage of hydrocarbon reservoirs around the world is fractured. Moreover, the major part of gas reservoirs in Iran is also fractured type, so the existence of an in-house software is necessary. In this study, an efficient, user-friendly, and indigenous simulation of a three-dimensional black oil fractured dry gas reservoir has been developed through IMPES method with the two-phase flow of gas and water. The presented simulator, which was written by C++ language and was known as fracture dry gas reservoir simulator, uses the implicit pressure and explicit saturation method for solving the equations. Also, effect of gravity pressure is neglected and effect of the capillary is considered in equations. By this simulator, we can investigate the dry gas reservoirs behavior with fractures. Darcy or non-Darcy fracture and matrix flow, Cartesian, cylindrical, and combination of Cartesian–cylindrical reservoir gridding, single porosity, dual porosity–single permeability, and dual porosity–dual permeability modeling are abilities of this simulator too. Additionally, this simulator is able to make outputs (such as pressure) at any given specific radius and time interval as numerical and/or graphical output in so little run time. Also, this simulator has PVT box and gridding box for doing the calculation of PVT and gridding. PVT box contains new correlations and EOS in comparison with another reservoir simulator. Gridding box makes us be able to simulate fractured dry gas reservoirs and hydraulically fractured well reservoirs too. Finally, the validity of this simulator was verified by comparing the simulation results with the other reservoir simulator (Eclipse) and showed a good compatibility between the developed software and Eclipse results in each time with different conditions such as various gridding conditions, various fluid data conditions and also various well configuration conditions.

A New Method to Determine the Grid Directions in Reservoir Numerical Simulation

Grid direction selection and grid size design are two important elements that need to be considered in the grid direction design in reservoir numerical simulation. Reservoir engineers normally utilize geological data (such as the distribution of fractures, low permeability zones, faults and major stress) and simulation experiences to design the grid direction of simulation model qualitatively. The research of the paper indicates that the key to determine the grid direction is to determine the principal permeability direction. Under the circumstances of few static materials, a new grid direction determination method has been developed by using field data (well location map and inter-well permeability) on the bases of Darcy’s law and tensor analysis theory. The grid direction of WZ11-7 Oilfield simulation model has been determined using four production wells and two production zones (L1 and L3) in WZ11-7-2 well group, the results are in conformity with the geological studied major stress. Therefore, this method can give insights into the numerical simulation study.

Estimating the chance of success of information acquisition for the Norne benchmark case

A key decision in field management is whether or not to acquire information to either improve project economics or reduce uncertainties. A widely spread technique to quantify the gain of information acquisition is Value of Information (VoI). However, estimating the possible outcomes of future information without the data is a complex task. While traditional VoI estimates are based on a single average value, the Chance of Success (CoS) methodology works as a diagnostic tool, estimating a range of possible outcomes that vary because of reservoir uncertainties. The objective of this work is to estimate the CoS of a 4D seismic before having the data, applied to a complex real case (Norne field). The objective is to assist the decision of whether, or not, to acquire further data. The methodology comprises the following steps: uncertainty quantification, selection of Representative Models (RMs), estimation of the acquisition period, production strategy optimization and, finally, quantification of the CoS. The estimates use numerical reservoir simulation, economic analysis, and uncertainty evaluation. We performed analyses considering perfect and imperfect information. We aim to verify the increment in economic return when the 4D data identifies the closest-to-reality reservoir model. While the traditional expected VoI calculation provides only an average value, this methodology has the advantage of considering the increase in the economic return due to reservoir uncertainties, characterized by different RMs. Our results showed that decreased reliability of information affected the decision of which production strategy to select. In our case, information reliability less than 70% is insufficient to change the perception of the uncertain reservoir and consequently decisions. Furthermore, when the reliability reached around 50%, the information lost value, as the economic return became similar to that of the case without information acquisition.

Novel Enhanced–Oil–Recovery Decision–Making Work Flow Derived From the Delphi–AHP–TOPSIS Method: A Case Study

SummaryThe process for the enhanced-oil-recovery (EOR) strategic decision is a thorny process that depends on proper evaluation, rational understanding of complex relationships, and quantitative assessment of multiple categories, including economic considerations, dynamic field production, reservoir numerical simulation, and other relevant elements. Using the Delphi-AHP-TOPSIS method (Joshi et al. 2011), which combines the Delphi method, analytic hierarchy process (AHP), and the technique for order preference by similarity to ideal solution (TOPSIS), we develop and present in this paper a decision-making work flow to meet this challenge.This technological process is divided into three phases. The first phase is the Delphi method, in which key performance factors and subfactors are identified and quantitatively evaluated using expert judgment. The second is the AHP, in which a synthesis of the influence of factors and subfactors is acquired with a pairwise-comparison matrix. The third phase is the TOPSIS, in which the final ranking of candidate EOR projects is given using the monotonic utility function. A typical polymer-flooding EOR decision-making case is presented for better understanding.The proposed work flow helps geoscientists acquire a comprehensive understanding of the factors in an existing project and select the optimal alternative. The approach not only minimizes the decision makers’ bias, combining the influence of technical and nontechnical factors, but also helps practitioners respond quickly to the oil market and reservoir dynamic performance rationally. This is, to our knowledge, the first time that the Delphi-AHP-TOPSIS method has been introduced to EOR decision making in the petroleum field.

The Researches on Reasonable Well Spacing of Gas Wells in Deep and low Permeability Gas Reservoirs

This Gs64 gas reservoir is a condensate gas reservoir which is relatively integrated with low porosity and low permeability found in Dagang Oilfield in recent years. The condensate content is as high as 610g/m3. At present, there are few reports about the well spacing of similar gas reservoirs at home and abroad. Therefore, determining the reasonable well spacing of the gas reservoir is important for ensuring the optimal development effect and economic benefit of the gas field development. This paper discusses the reasonable well spacing of the deep and low permeability gas reservoir from the aspects of percolation mechanics, gas reservoir engineering and numerical simulation. considering there exist the start-up pressure gradient in percolation process of low permeability gas reservoir, this paper combined with productivity equation under starting pressure gradient, established the formula of gas well spacing with the formation pressure and start-up pressure gradient. The calculation formula of starting pressure gradient and well spacing of gas wells. Adopting various methods to calculate values of gas reservoir spacing are close to well testing' radius, so the calculation method is reliable, which is very important for the determination of reasonable well spacing in low permeability gas reservoirs.

Quantification of a maximum injection volume of CO2 to avert geomechanical perturbations using a compositional fluid flow reservoir simulator

Abstract Subsurface CO2 injection and storage alters formation pressure. Changes of pore pressure may result in fault reactivation and hydraulic fracturing if the pressure exceeds the corresponding thresholds. Most simulation models predict such thresholds utilizing relatively homogeneous reservoir rock models and do not account for CO2 dissolution in the brine phase to calculate pore pressure evolution. This study presents an estimation of reservoir capacity in terms of allowable injection volume and rate utilizing the Frio CO2 injection site in the coast of the Gulf of Mexico as a case study. The work includes laboratory core testing, well-logging data analyses, and reservoir numerical simulation. We built a fine-scale reservoir model of the Frio pilot test in our in-house reservoir simulator IPARS (Integrated Parallel Accurate Reservoir Simulator). We first performed history matching of the pressure transient data of the Frio pilot test, and then used this history-matched reservoir model to investigate the effect of the CO2 dissolution into brine and predict the implications of larger CO2 injection volumes. Our simulation results –including CO2 dissolution– exhibited 33% lower pressure build-up relative to the simulation excluding dissolution. Capillary heterogeneity helps spread the CO2 plume and facilitate early breakthrough. Formation expansivity helps alleviate pore pressure build-up. Simulation results suggest that the injection schedule adopted during the actual pilot test very likely did not affect the mechanical integrity of the storage complex. Fault reactivation requires injection volumes of at least about sixty times larger than the actual injected volume at the same injection rate. Hydraulic fracturing necessitates much larger injection rates than the ones used in the Frio pilot test. Tested rock samples exhibit ductile deformation at in-situ effective stresses. Hence, we do not expect an increase of fault permeability in the Frio sand even in the presence of fault reactivation.

Distribution and Presence of Polymers in Porous Media

In order to better utilize the residual polymers formed after polymer flooding, the distribution and the presence of the polymers after polymer flooding were studied. This paper studied the vertical and plane distribution of the hydrophobically-associating polymer in addition to measuring the parameters after polymer flooding, which is important for numerical reservoir simulation. The results showed that the polymers mainly enter into the high permeability zone and distribute in the mainstream line area with only a small portion in the wing area. Based on the comparison of various experimental methods, double-slug experiments were chosen to measure the inaccessible pore volume and retention, which is considered to be the most accurate, most time-consuming and most complex method. Following this, we improved the processing method of experimental data by reducing it to one experiment with two parameters. At the same time, we further enhanced the accuracy of the experimental results. The results show that at 1750 mg/L, the inaccessible pore volume of the polymer is 25.8%. When the detention is 68.2 µg/g, the inaccessible pore volume constituted 22% of the total polymer, with the other 77.7% being the dissolved polymer. Moreover, the static adsorption and dynamic detention were measured, with the results showing that the static adsorption is larger than dynamic detention. Therefore, in the numerical reservoir simulation, using the static adsorption capacity instead of the dynamic detention is unreasonable. The double-slug method was chosen since it is more accurate for the determination of various parameters. Meanwhile, in order to enhance the accuracy of results, we improved the treatment of data.