Flowing Bottomhole Pressure Research Articles

Transient modeling of plunger lift with check valve for gas well deliquification in horizontal well

ABSTRACT To improve the plunger gas lift efficiency, a modified plunger was developed by adding a check valve to the downhole positioner in the Changqing Gas Field. However, the transient flow behavior of the plunger lift with check valve is unclear owing to the lack of a reliable method. Therefore, in this study, a simplified transient mechanistic model based on the law of conservation of momentum and mass is proposed to simulate the comprehensive dynamic process of a plunger-lift system. The results of the proposed method exhibited a high coincidence with that of the accumulated gas and liquid production of gas Well-1 in one cycle, and the relative errors were 2.11% and 4.29%, respectively. The calculation results demonstrate that the plunger lift with check valve can effectively prevent the flow of liquid to the bottom of the well, reduce the bottom hole flow pressure, and increase the productivity of gas well during the shut-in period. The efficiency of the plunger lift with check valve was enhanced with an increase in the volume of the accumulated liquid unloaded from the bottom of gas wells. Additionally, for the plunger lift with check valve, the production peak value was higher and the instantaneous gas production peak value was lower in comparison with the conventional plunger. After the plunger reached the wellhead, the instantaneous fluid production of the conventional plunger was relatively unstable than that of the plunger lift with check valve. A certain distance should be maintained from the bottom of the well in the case of the positioner with check valve. If the positioner is placed at the bottom of the tubing, the difference between the lift efficiency of the plunger with check valve and conventional plunger is small, and lower than that of the conventional plunger. The installation position of the downhole positioner in a gas well should be below the accumulated liquids level and should maintain a certain distance between the positioner and bottom of the well (200 m). The results of this study enhance the understanding of the operation mechanism of the plunger lift with check valve, and unloading of the accumulated liquids from the bottom of gas wells.

Calculation Model of Bottom Hole Flowing Pressure of Double-Layer Combined Production in Coalbed Methane Wells

Bottom hole flowing pressure (BHFP) is the key factor to determine a reasonable working system and achieve long-term stable production of coalbed methane (CBM) wells. However, there is no special BHFP model for double-layer combined production (DLCP). Generally, the constant mass model (CMM) for single-layer production is applied to treat the double reservoirs as a whole, ignoring the changes of fluid mass in each section and the acceleration pressure drop in the reservoir section. The calculation results have great errors, and the BHFP of the lower reservoir is used to adjust the production system of the two reservoirs, which does not meet the requirements of the upper reservoir. In this paper, the expression of acceleration pressure drop assumed to be zero in CMM is decomposed and derived, the relationship between acceleration pressure drop and unit length radial flow is established, and then the pressure drop formula of reservoir section with radial inflow is obtained. The reservoir is divided into several sublayers, and the pressure drop equation for each sublayer is established. According to the water flow and gas flow in the reservoir and nonreservoir sections, the corresponding velocity equations of water phase and gas phase are derived. The above equations are combined to establish the variable mass model (VMM) for DLCP with three stages. The field data are substituted into the VMM and the CMM, and the accuracy of the new model is verified. The results show that in the stages of double-layer water production and double-layer gas production, the errors of the two models are less than 5%, while in the stage of gas-water coproduction, the error of the VMM is 2.75%-6.58%, and the error of the CMM is 7.15%-15.18%. The VMM is more accurate. In addition, in the stages of water production and gas-water coproduction during DLCP, the BHFP of the two reservoirs differs greatly, with a maximum difference of 49.1%. Therefore, the two reservoirs need to adjust the production rule according to their respective BHFP. To sum up, the VMM can accurately give the BHFP of each reservoir, which is more realistic. It also solves the problem that one BHFP cannot accurately adjust the production rule of the two reservoirs, so as to provide technical support for the formulation of optimal production rule and the realization of high production.

An Explicit Method for Estimating the Average Reservoir Pressure and Gas in Place Based on the Gas Property Polynomial Function

The fast and accurate determination of average reservoir pressure (ARP) and gas reserves is vital for the analysis and forecasting of gas well performance. An explicit and relatively rigorous method for estimating the ARP and gas in place, based on the dynamic material balance equation and the gas property polynomial function (GPPF), is presented to circumvent such drawbacks as high cost, empiricism, poor adaptability to variable production schedules, and the necessity for iteration processes, by which current approaches usually have been limited. The gas flow model is solved by introducing pseudofunctions and considering the compressibility effects of rock and irreducible water, and the pressure-rate correlations during boundary-dominated flow (BDF) which constitute the theoretical proofs of this method are derived from the superposition principle coupled with the constant rate solution. Production data under different production scenarios prove it effective. The error of estimation for formation pressure and gas reserves hardly ever goes beyond 4% given that the BDF condition is generally satisfied by the rate and bottom hole flowing pressure (BHP) data. We employ the GPPF to capture the nonlinear variations of gas viscosity andZ-factor, helpful to implement a quick conversion between pressure and pseudopressure and to solve the integral equation concerning pseudopressure and thus valid in overcoming the limitations of previous methods concerning pressure or pressure squared. The proposed methodology boasts its simpleness and practicability, which not only dispenses with iterations on pseudotime but also applies to various production systems of gas wells under constant BHP, constant rate, or variable BHP/variable rate conditions.

Methane emission characteristics and model of CBM wells

CBM (CBM) development in coal mining area has good safety, economic and energy benefits, but methane emission is common in surface well sites. In view of this, taking the CBM wells in Qinshui County of Shanxi Province as an example, the methane emission location and emission volume of CBM wells are obtained, and the prediction model of methane emission volume is established through field investigation and multiple regression analysis. The results show that 81% of CBM wells have methane emission. Wellhead and drainage outlet are the two main positions of methane emission. The proportion of wells with emission volume between 0 and 10 m3/d is 59%. On the whole, the emission accounts for 1.53% of the gas well production. There is almost no methane emission in the single liquid phase flow stage. The methane emission in the gas-liquid two-phase flow stage is positively correlated with the bottom hole flow pressure, times of stroke and gas production, and negatively correlated with the pump embedment and water production. The bottom hole flow pressure and the pump embedment have the greatest impact on the methane emission. In the single-phase gas flow stage, methane emission is positively correlated with gas production and casing pressure, and negatively correlated with stroke frequency and water production.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure (FBHP) in wellbores is an important factor required for optimal tubing design and production optimization. Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters. Most machine learning (ML) models for FBHP prediction are developed with real-time field data but presented as black-box models. In addition, these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source. These make using the ML models on new datasets difficult. This study presents an artificial neural network (ANN) visible mathematical model for real-time multiphase FBHP prediction in wellbores. A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model. The data points were randomly divided into three different sets; 70% for training, 15% for validation, and the remaining 15% for testing. Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm (trainlm), hyperbolic tangent activation function (tansig), and three hidden layers with 20, 15 and 15 neurons in the first, second and third hidden layers respectively achieved the best performance. The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases. Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP. Furthermore, statistical and graphical error analysis results show that the new model outperformed existing empirical correlations, mechanistic models, and an ANN white-box model. Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

Optimization experimental design of stable production life and estimated ultimate recovery in gaseous compound of hydrogen and carbon from shale reservoir

In this study, a mixed numerical model is established to simulate the production performance of gaseous compound of hydrogen and carbon in shale reservoir. Estimated ultimate recovery (EUR) and stable production life (SPL) of gaseous compound of hydrogen and carbon are affected by reservoir parameters and production control parameters, such as Langmuir pressure, Langmuir volume, critical desorption pressure, fracture half length, permeability in stimulated reservoir volume (SRV) area, bottom hole flow pressure and production of gaseous compound of hydrogen and carbon. Therefore, in the later research, in order to reduce the number of numerical experiments, the orthogonal experimental method is used to optimize the influence of hydrocarbon ERU and SPL, and analyze the main control factors affecting these two indices at the same time. The purpose of this paper is to optimize the production of hydrocarbons by orthogonal experimental method and find the main controlling factors affecting the production of compound of hydrogen and carbon. Two groups of orthogonal experiments were designed to find the best scheme in the process of compound of hydrogen and carbon development. The calculative results show that the output of hydrogen and carbon compounds has a highly significant impact on SPL, Langmuir volume and capability in SRV have a significant impact, and Langmuir pressure Critical destruction pressure and hydraulic fracture half-length had no significant effects.

Errors in Initial Pressure Measurement Affect Rate-Transient Analysis

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 208321, “Effect of Errors in Initial Pressure Measurement on Rate-Transient Analysis in Unconventional Reservoirs,” by Himanshu S. Jha, SPE, Texas A&M University; Aaditya Khanal, The University of Texas at Tyler; and W. John Lee, SPE, Texas A&M University. The paper has not been peer reviewed. This paper presents a mathematical analysis of how incorrect estimates of initial reservoir pressure (pi) may affect rate-transient analysis (RTA) in ultralow-permeability reservoirs. Measured values of pi are rarely available, and the value estimated using alternative approaches, such as a diagnostic fracture injection test (DFIT), can be time-consuming and expensive and also may be inaccurate. In the complete paper, the authors examine what can and cannot be achieved with accuracy when reliable estimates of pi are not available. Introduction RTA and production forecasting using historical production data require flow-regime identification for model parameter calculation. The task of flow-regime identification is best achieved under constant-rate flowing conditions. However, field conditions and operational changes almost always cause flowing bottomhole pressure (BHP) to change during production. Use of rate-normalized pressure (RNP) can help mitigate the effect of changing BHP under smoothly changing conditions. Calculation of RNP requires an accurate estimate of pi, which often is unavailable. The authors’ work shows that the slope of a log-log plot of RNP vs. time or material balance time (MBT) is relatively insensitive to the value of pi. However, a Cartesian plot of RNP vs. timen (where n may be 0.5 for transient linear flow or a different value for more general “power-law behavior”) rotates the expected straight line for different values of pi or used in calculation of RNP. Therefore, the task of flow-regime identification can be achieved with reasonable certainty with only an estimate of initial pressure, but analysis with RNP vs. timen Cartesian plots may be unreliable.

Predicting the effects of selected reservoir petrophysical properties on bottomhole pressure via three computational intelligence techniques

This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure. To efficiently situate the essence of this investigation, genetic, imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables. A total of 15,633 data sets were collected from Volvo field in Norway, and after screening the data, a total of 9161 data sets were used to develop apt computational intelligence models. The data were randomly divided into three different groups: training, validation, and testing data. Two case scenarios were considered in this study. The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables, while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties (porosity and permeability). Each of the two scenarios involved as implied in the first scenario, the use of three (3) heuristic search optimizers to determine optimal model architectures. The optimizers were allowed to choose the optimal number of layers (between 1 and 10), the optimal number of nodal points (between 10 and 100) for each layer and the optimal learning rate required per task/operation. the results, showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001, although this became successively slower as the leaning rate decreased. With the chosen model configuration, the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets. Comparing the three heuristic search optimizers based on minimum MSE, RMSE, MAE and highest coefficient of determination (R2) for the actual and predicted values, shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately relative to the genetic and whale optimization algorithm optimizers.

Machine learning prediction of bottomhole flowing pressure as a time series in the volve field

Bottomhole flowing pressure (BHFP) is a critical parameter in analyzing oil and gas well performance, production forecasting and reservoir management. This study is focused on obtaining feature combinations towards low-error prediction of time-series BHFP in two wells in the Volve field. Three machine learning (ML) models (support vector regression (SVR), a distance-based model; random forest (RF), a tree-based ensemble model and Long Short-Term Memory (LSTM), a type of recurrent neural network) are used for BHFP prediction in two wells of the Volve field. The data for each well was split such that the first 70% is used in training the model, the next 15% as validation data for selecting the optimal hyperparameters and the last 15% for testing the models. The train and validation sets were used to train the models before making predictions on the test sets. While the SVR and RF models reasonably predicted the BHFP in both wells with a maximum Mean Absolute Percentage Error (MAPE) of 5.0% and 4.3% respectively, the LSTM model performed best across both wells with the MAPE less than 2.9% in both wells. ML model performance was superior for the well with the data distributed more uniformly. The three feature combinations with superior ML model performance for BHFP prediction all have five features in common namely: bottomhole temperature, oil flow rate, gas flow rate, choke size, onstream hours. The workflow in this work can be adopted for fieldwide BHFP prediction.

Rate-Pseudopressure Deconvolution Enhances Rate-Time Models Production History-Matches and Forecasts of Shale Gas Wells

Summary Physics-based and empirical rate-time models inherently assume constant bottomhole flowing pressure (BHP), an assumption that may not hold for many unconventional wells. Hence, applying these models without accounting for BHP variations might lead to inaccurate (a) flow regime identification, (b) estimation of the parameters of these models, and (c) estimated ultimate recovery (EUR) and drainage volumes. This study evaluates and compares the predictions of rate-time relations including and ignoring corrections for time-varying BHP for both synthetic and shale gas wells. We generate a real gas synthetic case with errors in the time-varying BHP. First, we convert pressures into pseudopressures. Second, we deconvolve the pseudopressure history by applying the regularized exponential basis function inverse scheme to obtain an equivalent rate—the unit-pseudopressure-drop rate at standard conditions—at constant BHP. Third, we history match the production using the scaled single-phase compressible fluid physics-based model for three different approaches: (a) using rate-time-pressure data with rate-pseudopressure deconvolution, (b) using rate-time-pressure data using just rate-pressure deconvolution, and (c) using only rate-time data. Finally, we compare the results in terms of their history matches and estimated reservoir parameters. We conclude by illustrating the application of this procedure to shale gas wells. For the synthetic case, the fit of the single-phase compressible fluid rate-time model using rate-pseudopressure deconvolution can accurately estimate the original gas in place, characteristic time, gas permeability, and fracture half-length. In contrast, considerable errors are noted when either using rate-pressure deconvolution or failing to account for variable BHP. Regarding the shale gas examples, the rate-pseudopressure deconvolution scheme accurately identifies the flow regimes present in the well, which can be difficult to detect by only analyzing rate-time data. For this reason, the fits of the scaled single-phase compressible fluid model using only rate-time result in unreasonably large estimates of the reservoir parameters and EUR. In contrast, the application of rate-pseudopressure deconvolution constrains the fits of the single-phase compressible fluid model yielding more realistic estimates of the time of end of transient flow, and EUR. This paper illustrates the application of a workflow that accounts for variable BHP by estimating an equivalent constant unit-pseudopressure-drop gas rate (at standard conditions). We illustrate the workflow for a particular decline-curve model, but the workflow is general and can be applied to any rate-time model. The approach history matches and forecasts the production of unconventional gas reservoirs using rate-time models more accurately than assuming constant BHP.

Sustainable dewatering of unconventional gas wells using engineered multiphase flow dynamics

This paper aims to investigate and demonstrate, for the first time, the feasibility of using an artificial lift system, referred to as foam assisted gas lift (FAGL) for dewatering unconventional gas wells to depressurise coal and allow gas to be produced. More specifically, this paper investigates the capability of a three-phase foam flow to lift gas, liquid and solid particulates to the surface to optimise gas production by minimising the flowing bottom-hole pressure (FBHP) and minimising downtime, operational costs and production losses caused by downhole pump failures.A laboratory set-up of a 9 m vertical tubing with 80 mm (3.1 in) ID and a 7 m annulus with a casing inner diameter of 170 mm (6.69 in) and tubing outer diameter of 70 mm (2.76 in) are used to investigate the foam flow behaviour in both geometries for a wide range of gas and water flowrates, 40–6500 L/min and 10–80 L/min, respectively. The 80 mm tubing is pressurised up to 6 bar, allowing examining the foam flow characteristics and foam stability at different depths of a gas well, by changing the operating pressure. Foam flow regimes in the tubing and the annulus, which are key in determining the FBHP are characterised by analysing differential pressure signals, collected at 100 Hz frequency. The addition of foamer (sodium dodecylbenzene sulfonate) significantly advanced the transition from churn to the annular flow regime, resulting in a lower required gas flow rate to obtain the least attainable FBHP. A model is developed to evaluate the FBHP of the FAGL, compared against the measured FBHP in 36 gas wells in Australia, equipped with down-hole pumps for dewatering. Our results indicate that FAGL is capable of maintaining a low FBHP for a wide range of gas and water flow rates. The effective particle lifting capacity of foam flow is also demonstrated. Further, the positive impact of water salinity (0.1 M NaCl) in the presence of particles (15–25 g/l) on foam integrity is presented.

Productivity prediction with consideration of stress-sensitive permeability in naturally fractured carbonate reservoir

The aim of this paper is to illustrate the impact of stress-sensitive permeability on productivity in naturally fracture carbonate reservoirs. First, stress sensitivity evaluation experiments were conducted on artificial cores with matrix, uncemented fracture, partially- cemented fracture and fully-cemented fracture by measuring their permeability in the process of increasing confining pressure. Then, a new mathematical model for productivity prediction in naturally fractured carbonate reservoir with consideration of stress-sensitive permeability is developed. Comparisons have been made between the new model prediction results and field measured data to verify the accuracy of the new model. Finally, based on the validated model, the detailed impact of permeability modulus on productivity is analysed. The experiment results show that the strength of stress sensitivity, evaluated by permeability modulus, of cores with uncemented fracture and partially-cemented fracture is generally an order of magnitude larger than that of cores with matrix and fully-cemented fracture. The results show the new model can be simplified to the classic model (Vogel equation) under the condition of the stress sensitivity being not taken into account, which means the class model is corresponding to a special case of this new model. Field application shows that the new model is reliable and correct. Moreover, with the help of the new model, it is found that the impact of stress-sensitive permeability on productivity increases with increasing permeability modulus and decreasing flow bottom hole pressure.

Determination of Reasonable Bottom-Hole Pressure in Unconventional Tight Oil Reservoirs: A Field Case Investigation

Abstract The bottom-hole flow pressure is an important factor affecting the production of the unconventional tight oil well. The reasonable bottom-hole flow pressure has a positive effect on enhancing the production capacity of oilfields and improving the production efficiency of oilfield enterprises. Therefore, a robust method is proposed to determine the reasonable bottom-hole flow pressure in unconventional tight oil reservoirs, sharing the advantages of coupling geological feature and fluid flow behavior. In this method, the geological model and numerical reservoir simulation based on ECLIPSE software are developed firstly. The effects of the stress sensitivity, the PVT properties, permeability damage, fracturing fluid leak-off, and skin coefficient on the single-well productivity are then discussed. The reasonable variation range of the bottom-hole flow pressure in different production stages is then determined. Results show that crude oil viscosity, formation damage, and stress sensitivity are the most sensitive factors to the recovery degree. And, desirable tight oil production performance can be achieved with the application of the optimized bottom-hole pressure.

Production Behavior Analysis of Multibranched Horizontal Oil Well considering Reservoir and Well-Type Factors

Multibranched horizontal well is an important means to develop low permeability reservoirs. Fishbone multibranched horizontal well has the advantages of increasing drainage area, reducing drilling number, utilizing existing wells, and saving oilfield development cost, especially for marginal oilfield exploitation. The morphological structure of fishbone multibranched horizontal well is very complex, so the numerical simulation study is of great significance to guide the production of fishbone multibranched horizontal well. In this paper, the numerical model is established for fishbone multibranched horizontal well in the oil reservoir. The finite element method is used to numerically solve the mathematic model. The oil well production can be achieved by using the material balance method. Sensitivity analysis is made on the important reservoir and well-type factors that affect the production behavior and transient pressure distribution of fishbone multibranched horizontal well. It is concluded that the effective reservoir thickness and flowing bottomhole pressure have great influence on the productivity, but the influence of heterogeneity is not obvious. The length of main wellbore has great effect on the productivity in the early stage. Fishbone multibranched horizontal wells should be placed in the middle of the reservoir to increase productivity. Branch length, branch angle, branch number, and branch spacing are important parameters affecting the productivity of fishbone multibranched horizontal well. The variation of these parameters has obvious influence on the stimulation effect in the early stage of production, but the influence degree is different. Under the premise of drilling technology and drilling safety, the comprehensive impact of these four factors on productivity should be considered simultaneously. The presented model and obtained results not only enrich production behavior analysis of fishbone multibranched horizontal well but also have significance on formulation of stimulation measures and efficient low permeability reservoir development.

Storage Ratio of CO2 Miscible Flooding in Chang 8 Reservoir of H Block in Ordos Basin Under Different Injection Methods

Carbon dioxide is a typical kind of greenhouse gases, and the oil and gas field recovery ratio could be significantly improved by injecting it into the formation. The main goal is to increase the recovery ratio in the early stage of studying gas injection miscible flooding. With the advancement of the “carbon neutral” strategy in China, the application of CO2 miscible flooding technology, which aims to enhance the oil recovery ratio in Chinese oilfields, has been further expanded. The development concept needs to change from simply improving the recovery ratio to improving both the recovery ratio and the CO2 storage ratio. In the current CO2 flooding research, people mainly focus on the improvement of the recovery factor, and less attention is paid to the underground storage of CO2. In order to further study the relationship between the displacement efficiency of CO2 and the storage situation, this paper innovatively combines the storage ratio and storage amount of CO2 with the recovery factor. A number of parameters under different injection methods in the study area were calculated, providing a new research angle for the study of CO2 displacement. Take Chang 8 reservoir in Block H of Ordos Basin as an example. Firstly, the minimum miscibility pressure (MMP) of CO2 at formation temperature was measured by the slim tube experiment according to the reservoir pressure status to judge the CO2 flooding status in the study area. Then, the numerical simulation model of the study area was established, and the historical fitting was completed according to the existing CO2 flooding test wells. The reservoir numerical simulation is used to study the recovery ratio and CO2 storage ratio under different CO2 miscible flooding, including different injection rates, injection sequences, and bottom hole pressures. The results show that the MMP of CO2 in the study area is between 12.65 and 14.80 MPa, which means the CO2 flooding state in this area is miscible flooding with high oil displacement efficiency. The CO2 storage ratio at 5–40 t/d CO2 injection rate is between 54.02% and 64.38% after 20 years, and the CO2 storage ratio is larger when the gas injection rate is 10 t/d. In combination with CO2 storage capacity and the oil recovery factor, it is recommended that the gas injection rate of a single well in the study area is 20–30 t/d. The CO2 storage ratio after 20 years under different gas injection sequences is 57.69%–61.27%. It is suggested that the study area should be injected with water and gas alternately after 2 years of gas injection. When continuous gas injection is used, the CO2 storage ratio under different production well bottom hole pressures is between 54.95% and 59.82%. It is recommended that the bottom hole flow pressure in the study area be maintained at between 9 and 10 MPa. The results show that the main factor that affects the annual CO2 storage ratio in the study area is the CO2 injection rate. Gas injection rate, injection sequence, and bottom hole pressure of production wells will all have an impact on the overall storage ratio. And alternate water and gas injection is quite significant. From the perspective of storage ratio, the optimal CO2 miscible flooding injection method has been optimized for the study area, which provides a reference for the implementation of CO2 underground storage technology in mines.

Scale Up of Pore-Network Models into Reservoir Scale: Optimization of Inflow Control Devices Placement

Summary Improvements to more advanced tools, such as inflow control devices (ICDs), create a high drawdown regime close to wellbores. Gas liberation within the formation occurs when the drawdown pressure is reduced below the bubblepoint pressure, which in turn reduces oil mobility by reducing its relative permeability, and potentially reducing oil flow. The key input in any reservoir modeling to compare the competition between gas and liquid flow toward ICDs is the relative permeability of different phases. Pore-network modeling (PNM) has been used to compute the relative permeability curves of oil, gas, and water based on the pore structure of the formation. In this paper, we explain the variability of pore structure on its relative permeability, and for a similar formation and identical permeability, we explain how other factors, such as connectivity and throat radius distribution, can vary the characteristic curves. By using a boundary element method, we also incorporate the expected relative permeability and capillary pressure curves into the modeling. The results show that such variability in the pore network has a less than 10% impact on production gas rates, but its effect on oil production can be significant. Another important finding of such modeling is that providing the PNM-created relative permeabilities may provide totally different direction on setting the operational constraints. For example, in the case studied in this paper, PNM-created relative permeability curves suggest that a reduction of flowing bottomhole pressure (FBHP) increases the oil rate, but for the case modeled with a Corey correlation, changes in FBHP will not create any uplift. The results of such work show the importance of PNM in well completion design and probabilistic analysis of the performance, and can be extended based on different factors of the reservoir in future research. Although PNM has been widely used to study the multiphase flow in porous media in academia, the application of such modeling in reservoir and production engineering is quite narrow. In this study, we develop a framework that shows the general user the importance of PNM simulation and its implementation in day-to-day modeling. With this approach, the PNM can be used not just to provide relative permeability or capillary pressure curves on a core or pore- scale, but to preform simulations at the wellbore or reservoir scale as well to optimize the current completions.

Techniques for History-Matching and Forecasting Tight Oil Reservoirs Compared

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTEC 208352, “Assessment of Rate-Normalization and Pressure-Deconvolution Techniques To History-Match and Forecast Production of Tight Oil Reservoirs Using a Physics-Based Rate-Time Model,” by Leopoldo M. Ruiz Maraggi, SPE, Larry W. Lake, SPE, and Mark P. Walsh, SPE, The University of Texas at Austin. The paper has not been peer reviewed. Rate transient analysis (RTA) is used routinely to analyze the production history of unconventional wells. Specifically, it is used to determine the time at the end of linear flow (time to depletion), estimate drainage volume, and predict the estimated ultimate recovery (EUR) of wells. RTA uses a rate-normalization technique to characterize how the well rate changes with a changing bottomhole flow pressure. However, rate normalization approximates a more-general technique known as pressure deconvolution. The complete paper evaluates and compares the performance of rate normalization and pressure deconvolution for both synthetic and tight oil examples. Overview The complete paper first presents the solution of the single-phase slightly compressible fluid-diffusivity equation for a certain set of idealized conditions. Second, the concepts of time superposition, convolution, deconvolution, and rate normalization are presented. Third, a numerical regularized exponential basis function inverse scheme is proposed for use in deconvolution. Fourth, the comparison of the rate-normalization and pressure-deconvolution techniques for a synthetic case are presented. Finally, the two methods are evaluated for tight oil wells. This synopsis will not include the many equations involved in these processes or detailed discussions thereof; instead, it concentrates on the work flow that the authors have developed, specifically its application to synthetic cases.

Influence Factors of the Bottom Hole Flow Pressure in a High-Pressure and High-Temperature Condensate Gas Reservoir: Applicability Analysis

Influence Factors of the Bottom Hole Flow Pressure in a High-Pressure and High-Temperature Condensate Gas Reservoir: Applicability Analysis

Numerical Assessment of the Impact of Sand Control Techniques on Production in a Vertical Well

This study considers a vertical well producing initially without sand control technique in sandstone reservoir in the Niger Delta region. The study uses the data and information of two wells and applied PROSPER to numerically quantify the impact of the extra flow restriction caused by the gravel pack and slotted liner systems respectively. Well model was constructed and several simulations runs performed on key influencing production parameters (production rates, superficial velocities, skin development, pressure losses) and sand control design variables (gravel pack length, gravel pack permeability, slot height and slot width). The study involved two different sand control for two wells (Well X1 and Well X2) which were similarly completed and subjected to the same reservoir and operational conditions. The influence of sand control parameters on the vertical lift performance and inflow performance characteristics of the wells were analyzed at varying first node pressure (500psig – 1400psig). The solution node was set at bottom hole to enable proper diagnosis of the influence of the sand control options on the VLP/IPR relationships. At base case scenario, the gravel pack oil and gas production rates were 8010.0STB/day (ORAT) and 6.008MSCF/day (GRAT) at a flowing bottomhole pressure of 4231.38Psig while the slotted liner was 8010.0STB/day (ORAT) and 6.008MSCF/day (GRAT) at a flowing bottomhole pressure of 4231.38Psig at a flowing bottomhole pressure of 4231.38Psig of 3902.46Psig. The resulting skin due to sand control were 0.0070375 and 0.18 respectively. Result shows that Slotted Liners provides better sand control than gravel packs but causes more pressure drop in the system due to sand control method. Furthermore, Gravel pack permeability and length and slot dimensions have most remarkable influence on the pressure drop due to sand control and can play a crucial role in the choice and design of any gravel pack system and slotted liners respectively. It’s advisable or better that Gravel pack permeability should not exceed 500000md for optimal performance as a sand control device. The work recommends the use of higher slot width to slot height for more efficient production using slotted liners while smaller gravel permeability could be preferred for better sand control with gravel pack systems.

Research on the Inflow Performance of the Plunger Lift in the Shale Gas Horizontal Well

Shale gas wells in the Changning block of Weiyuan, Sichuan, China, experience water production problems throughout their life cycles. In actual field production, plunger lift is typically used to discharge liquid from a shale gas well. However, it is difficult to test the formation inflow performance relationship curve (IPR) of a plunger lift well. Hence, data on the formulation and optimization of plunger lift well systems are lacking. Based on actual measured data related to casing pressure changes at the wellhead and the gas-liquid distribution characteristics in a wellbore during the shut-in phase, this paper establishes a method for calculating the formation IPR curve of a shale gas plunger lift well by calculating changes in wellbore gas volume continuously. This method has a fast calculation speed. The gas-water interface distribution can be obtained in 3–5 iterations, and the inflow dynamic curve can be calculated in less than 5 seconds. This method tested the IPR curves of gas wells in the Changning block. The IPR curves of plunger lift wells showed concave characteristics contrary to the conventional upward convex curves. This is because the bottom hole flowing pressure changes caused variations in the water saturation of the near-wellbore zone. Consequently, the productivity equation of the plunger lift well is established by introducing the variation coefficient of water saturation expressed by bottom hole flow pressure. This equation fits with the measured IPR curve with an average R2 of approximately 0.9989.