Bottomhole Pressure Research Articles

Experimental and Numerical Investigations of Low-Permeability Sandstone Under Water Injection–Induced Dilation in West Oilfield, South China Sea

With the development of offshore oil fields, the reservoirs of X oilfield in the west of the South China Sea are poor in physical property, serious in pollution, and increasingly prominent in interlayer contradictions. Water injection dilation technology has strongly affected the development of loose sandstone reservoirs. To explore whether this technology applies to the low-permeability sandstone of X oilfield in the west of the South China Sea and the dilation effect and radius of water injection dilation technology on the target reservoir, low confining pressure rock mechanics experiments and numerical simulation of water injection in this reservoir section are carried out. The triaxial shear experiment of low confining pressure shows that the target reservoir sandstone with low-permeability can have a shear strength of 45 MPa when the effective confining pressure is 0.5 MPa, and the target reservoir core can have dilatancy. When the axial strain is 2.5%, the core dilatancy is 1%, and the permeability changes by 1.17 times. It was found that the core volume dilation was obviously under low effective confining pressure, and the permeability is 2 orders higher than in the initial condition. The numerical simulation of the target reservoir shows that the bottom-hole pressure reaches 47.12 MPa at the end of water injection in typical wells. The reservoir was deformed to different degrees around the well, and the top layer was raised by 5.58 mm. This paper characterizes the rock expansion potential and expansion flow capacity of low-permeability sandstone reservoirs from multiple perspectives and establishes a three-dimensional, full-size wellbore formation crustal stress strict matching geological model for offshore expansion wells. We have provided theoretical guidance for on-site construction.

Evaluation and Optimization of Cement Slurry Systems for Ultra-Deep Well Cementing at 220 °C

With the depletion of shallow oil and gas resources, wells are being drilled to deeper and deeper depths to find new hydrocarbon reserves. This study presents the selection and optimization process of the cement slurries to be used for the deepest well ever drilled in China, with a planned vertical depth of 11,100 m. The bottomhole circulating and static temperatures of the well were estimated to be 210 °C and 220 °C, respectively, while the bottomhole pressure was estimated to be 130 MPa. Laboratory tests simulating the bottomhole conditions were conducted to evaluate and compare the slurry formulations supplied by four different service providers. Test results indicated that the inappropriate use of a stirred fluid loss testing apparatus could lead to overdesign of the fluid loss properties of the cement slurry, which could, in turn, lead to abnormal gelation of the cement slurry during thickening time tests. The initial formulation given by different service providers could meet most of the design requirements, except for the long-term strength stability. The combined addition of crystalline silica and a reactive aluminum-bearing compound to oil well cement is critical for preventing microstructure coarsening and strength retrogression at 220 °C. Two of the finally optimized cement slurry formulations had thickening times more than 4 h, API fluid loss values less than 50 mL, sedimentation stability better than 0.02 g/cm3, and compressive strengths higher than 30 MPa during the curing period from 1 d to 30 d.

Development of Machine Learning-Based Production Forecasting for Offshore Gas Fields Using a Dynamic Material Balance Equation

Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment.

Productivity analysis by insulation design of well with vacuum insulated tubing in SAGD process

This study aims to design insulation and demonstrate its performance on the vertical section of a well in a steam-assisted gravity drainage (SAGD) process. Utilizing the reservoir characteristics of the Athabasca oil sands in Canada, we analyzed oil productivity and derived optimal operating conditions through a multi-objective optimization algorithm. For the simulation, the well's vertical section was insulated with 0.006 W/m‧°C, the thermal conductivity of vacuum insulated tubing (VIT), exhibiting high insulation efficiency. The results indicated that cumulative production increased by up to 20.24%, and production efficiency improved. Additionally, sensitivity analysis using response surface methodology (RSM) revealed that the injection bottom-hole pressure (inj_BHP) was the most significant factor in the insulated well design when parameters varied. A multi-objective optimization using particle swarm optimization (PSO) was conducted to determine the optimal operating conditions, aiming to maximize cumulative oil production and minimize the cumulative steam-oil ratio (cSOR). The optimization resulted in a 39.8% improvement in cumulative oil production and an 88.19% reduction in cSOR, compared to the non-insulation scenario. The findings of this study are anticipated to serve as a guideline for wellbore insulation design in SAGD processes.

Research on Temperature–Pressure Coupling Model of Gas Storage Well during Injection Production

Periodic changes in wellbore temperature and pressure caused by the cyclic injecting and producing of gas storage wells affect wellbore integrity. To explore the distribution and influencing factors of wellbore temperature and pressure during gas storage well injection-production processes, based on energy conservation, momentum theorem, and the transient heat transfer mechanism of the wellbore, a temperature and pressure coupling model for gas storage injection-production wellbores was established, and a piecewise iterative method was used to solve the model equations. Compared with the field data, the predicted relative errors of the wellhead temperature and pressure were 2.30% and 2.07%, respectively, indicating that the coupling model has a high predictive accuracy. The influences of the injection-production conditions, tubing diameter, and overall heat transfer coefficient on the wellbore temperature and pressure distributions were analyzed through an example. When the gas injection flow rate increased by 1.5 times, the bottomhole temperature decreased by 37%. Doubling the overall heat transfer coefficient resulted in a 10% rise in the bottomhole temperature. An increase of 0.3 times in the gas injection pressure led to a 31% increase in bottomhole pressure. With a 1.5-fold increase in the gas production flow rate, the wellhead temperature rose by 28%, and the wellhead pressure dropped by 20%. The research in this paper can serve as a guide for the optimization design and safe operation of gas storage wells.

A Leakage Safety Discrimination Model and Method for Saline Aquifer CCS Based on Pressure Dynamics

The saline aquifer CCS is a crucial site for carbon storage. Safety monitoring is a key technology for saline aquifer CCS. Current CO2 leakage detection methods include microseismic, electromagnetic, and well-logging techniques. However, these methods face challenges, such as difficulties in determining CO2 migration fronts and predicting potential leakage events; as a result, the formulation of test timing and methods for these safety monitoring techniques are somewhat arbitrary. This study establishes a gas–water two-phase seepage model and solves it using a semi-analytical method to obtain the injection pressure and the derivative curve characteristics of the injection well. The pressure derivative curve can reflect the physical properties of the reservoir through which CO2 flows underground, and it can also be used to determine whether CO2 leakage has occurred, as well as the timing and amount of leakage, based on boundary responses. This study conducted sensitivity analyses on eight parameters to determine the impact of each parameter on the bottom-hole pressure and its derivatives, thereby obtaining the influence of its parameters on different flow stages. The research indicates that, when a steady-state flow characteristic appears at the outer boundary, CO2 leakage will occur. Additionally, the leakage location can be determined by calculating the distance from the injection well. This can guide the placement and measurement of safety monitoring methods for saline aquifer CCS. The method proposed in this paper can effectively monitor the timing, location, and amount of leakage, providing a technical safeguard for promoting CCS technology.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.

Drilling Fluid Density Optimization for Narrow Density Window Fractured Formation

Abstract Fractures are prevalent in the Leikoupo Formation in western Sichuan, with a narrow safe density window of 0.06-0.12 g/cm3, leading to frequent downhole accidents like kicks and losses. Using Pengzhou X well as a case study, this research employed numerical simulation to analyze the impact of various drilling fluid densities within this window on displacement rate and bottomhole pressure. It established an optimal drilling fluid density model for fractured formations with narrow density windows. Findings reveal that as drilling fluid density increases, annulus up-return velocity decreases, potentially trapping fracture gas in the upper annulus. Optimal drilling fluid density lies within this narrow window, maintaining a dynamic balance between preventing fast gas return and fluid accumulation in fractured formations. The model applied to Pengzhou Y well yields a drilling density correlation of 0.000001036, consistent with optimized model results. This approach optimizes drilling fluid density within narrow density windows, mitigating downhole gushing and leakage accidents.

Research of productivity and interlayer interference mechanism of multilayer co-production in reservoirs with multi-pressure systems

Abstract Due to the differences of physical and fluid properties in vertical oil layers, the interlayer contradictions are prominent and affect the reservoir utilization degree seriously. Therefore, the coordination between reasonable bottom-hole flow pressure and optimal production, as well as the reduction of interlayer interference are the primary concerns during the co-production of multi-layer oil reservoirs. Based on the reservoir engineering and fluid mechanics in porous media theory, this work adopt the comprehensive pressure system and consider the interlayer interference to establish a multi-branch horizontal well productivity model and interlayer interference mathematical model. By analyzing the main controlling factors and interference mechanisms, this article establishes a pattern of interlayer interference, and improves the quantitative characterization interlayer interference theory in multi-layer combined mining. The study has shown that: (1) The interlayer interference is beneficial for balancing the production of different layers and improving development efficiency, it is greatly affected by interlayer heterogeneity; (2) When the number of layers exceeds 2 layers, the interference coefficient increases; With the increase of the layer thickness, the thicker oil layers have higher productivity, and the thickness of layer has a significant effect on the production of low-pressure layer; When the production pressure difference increases, the interlayer interference can be reduced; (3) The interlayer interference mathematical model constructed in this paper has high prediction accuracy and strong practicality, which has theoretical guidance significance for the division of strata in comprehensive adjustment of reservoirs.

Investigation on Wax Deposition Risk in Shale Oil Well During Production Test

Abstract During production test of the shale oil well, the low temperature environment in the wellbore is likely to cause wax precipitation of shale oil, and in turn disrupt the normal production test, increase the operating costs and risks. In view of this problem, by considering the gas-liquid flow, radial heat transfer and temperature gradient etc, a temperature and pressure field model of shale oil wellbore was established, and a prediction method of wax deposition zone in the shale oil wellbore during production test was proposed. Furthermore, by using the production data of a shale oil well with wax deposition in the South China Sea during production test, the accuracy of the model in calculating the fluid temperature and pressure of the wellbore with wax deposition was verified. The wellhead temperatures calculated by the model had a maximum error of 9.31%, and the bottom-hole pressures calculated by the model had a maximum error of 7.52%, indicating the model has high calculation accuracy. The model was used to analyze the sensitivities of wax deposition to the time and rate of production, water content and the ratio of gas to oil. It is found that the wax deposited in wellbore increases in thickness with the production time, is more strongly affected by production rate and water content, but less affected by the ratio of gas to oil. The research results can provide support for preventing wax deposition during production test of shale oil wells.

Analysis of Carbon Dioxide Injectivity and Dynamic Storage Capacity in Shale Reservoirs

Abstract Carbon dioxide could be stored in unconventional shale reservoirs in supercritical state due to available pore volume, infrastructure, and injectivity. However, there is a lack of knowledge about the injectivity and storage capacity in shale reservoirs. In this manuscript, a two-dimensional dual-porosity, dual-permeability model was built to investigate CO2 injectivity and dynamic storage capacity spatially and temporally. Parametric studies are conducted to evaluate the effect of matrix permeability, fracture conductivity, fracture half length, operating conditions, and near-wellbore connectivity on storage factors. Systematic and comprehensive numerical experiments are carried out using random sampling to generate a probability distribution of CO2 storage factors and replacement ratio. Results showed the parameters with most impact to least impact on injectivity and storage factor: matrix permeability, near-wellbore connectivity, bottomhole pressure, fracture half length, and fracture conductivity. The methodology in this study provides a foundation to examine how CO2 storage factors change spatially and temporally in and outside the SRV. The new understanding can be applied to optimize field development, well spacing, and infill drilling to increase economic storage.

Study on the Equivalent Density Tool and Depressurisation Mechanism of Suction-Type Depressurisation Cycle

In order to further regulate equivalent circulating density (ECD), a novel downhole apparatus for reducing circulating pressure in high-temperature and high-pressure wells, the suction-type ECD reduction tool, was devised. The utilisation of this tool enables the bottomhole pressure of the equivalent circulating density to be attained in close proximity to its hydrostatic pressure, thereby facilitating the attainment of deeper drilling depths. The tool is composed primarily of a screw motor, scroll blades, annular seals, universal joints, and drilling columns. The tool operates by utilising the suction effect and hydraulic energy extracted from the circulating fluid by the screw motor, which is then converted into mechanical energy to create suction and enhance the flow energy of the drilling fluid within the annulus at the bottom of the well, thereby reducing the equivalent circulating density. Furthermore, based on ANSYS-FLUENT analysis simulations, the alteration of pressure drop characteristics in response to varying drilling fluid densities, displacements, and tool sizes was modelled. The simulation results demonstrate that the pressure drop effect is 1.0 MPa when the drilling fluid density is 1.2 g/cm3, 1.7 MPa when the drilling fluid density is 1.5 g/cm3, and 1.9 MPa when the drilling fluid density is 1.8 g/cm3. A pressure drop of approximately 2.3 MPa was observed when the drilling fluid density was 2.0 g/cm3. The maximum pressure drop is achievable with a flow rate ranging from 1500 to 2500 L/min. A maximum pressure drop of 2.3 MPa is observed when the flow rate is within the range of 1500 to 2500 L/min. Two distinct viscosity values (0.02 and 0.06 kg/(m·s)) were employed to assess the impact of viscosity on pressure drop characteristics in a suction-type ECD tool. The results demonstrated that the pressure drop remained largely unaltered, indicating that viscosity had minimal influence on this parameter. The flow rate emerged as the primary factor affecting pressure drop, with viscosity exerting a relatively minor effect.

Fracture propagation characteristics of layered shale oil reservoirs with dense laminas under cyclic pressure shock fracturing

Forming a fracture network through fracturing stimulation is significant to efficiently developing shale oil resources. However, the complex lithological characteristics and dense laminas of continental shale oil strongly shield fracture propagation. The concept of "cyclic fluid injection induces rock fatigue" was introduced into shale oil fracturing technology, and the cyclic pressure shock fracturing method was designed. The horizontal well fracturing simulation experiments used prepared shale rock samples from the Permian Lucaogou Formation shale oil reservoir outcrop in Jimusar Sag, Junggar Basin. Two pressurization states were obtained through constant injection and rapid release of accumulated high pressure, corresponding to conventional and pressure shock conditions. The characteristics of fracture propagation under different fracturing methods were analyzed by combining acoustic emission monitoring and injection pressure curve response. Research has found that the dense laminas with a certain original width near the wellbore significantly inhibit the vertical propagation of hydraulic fractures (HFs), and conventional constant-rate fracturing methods make it difficult to stimulate the reservoir effectively. Fatigue fracturing can increase the complexity of near-wellbore fractures, but the HFs still tend to be arrested by the laminas. The bottom hole pressure (BHP) is artificially increased to a value far exceeding the rock breakdown pressure near the wellbore by applying the cyclic pressure shock fracturing method. It can avoid the communication between micro-cracks and horizontal laminas during the BHP constant rate increase process and overcome the inhibition of weak layers on vertical propagation. Besides, the fracture height and number of activated laminas positively correlate with the number of cycles. When the shock pressure is about 30 MPa, the fracture height of the three cycles increases by 50% compared to a single shock. In addition, the shock pressure has a more significant effect on the fracture height, and the fracture height increases significantly with the increase of shock pressure. The shock pressure increased by about 57%, and the fracture height increased by 60%. High-pressure shock has a certain effect on the naturally weak surface of the far well, which may cause the slip of the weak surface to produce AE signals. This provides a new approach for improving fracture complexity and control volume in layered shale oil reservoirs with dense laminas.

APPROACH TO CREATION OF GEOLOGICAL AND RESERVOIR SIMULATION MODEL AND METHODS FOR HISTORY MATCHING OF RESERVOIR ENERGY STATE

Under conditions of complex geological structure and substantial share of hard-to-recover oil reserves, reservoir simulation is a key tool in reservoir management. Direct problems solved by reservoir simulation include forecasting of the production performance, control and management of reservoir development, and localization of residual oil reserves. Inverse problem solution is useful for updating the knowledge of geological structure, reservoir quality, and historical production data by means of history matching. The quality of history matching is fundamental to ensure reliable future forecasts and localization of residual oil zones. The main approaches to appropriate history matching rely on analysis and adjustment of well logging and geological parameters, as well as checking and correction of errors, inadequate historical data, application of physics-based and reasonable approaches and tools. The prerequisite for adequate history matching and predictive ability of the resultant reservoir simulation model is the compliance of simulated and actual energy state of the reservoir. In this paper, the initial stage of reservoir simulation is performed, the approach to history matching of reservoir energy state aimed at improving the quality of further predictions is described as exemplified by one of the production areas of Russian oil field. The main causes of poor history match of formation and bottomhole pressures are revealed. These causes are grouped as follows: — geological causes/reservoir quality, which include permeability field distribution, reservoir connectivity, and determination of rock and fluid compressibility;— technical causes (during reservoir simulation), that is model limitations in case of larger areas, the need to use sector models, consideration of buffer region;— technological causes, including inadequate historical data or lack of data, detection of behind-the-casing flows and production casing leaks in wellbore, etc. For each group of factors that have a substantial impact on reservoir pressure, recommendations and methods of energy state history matching on geological and reservoir simulation models are provided, examples are given.

DEVELOPMENT OF AN EXPRESS ASSESSMENT OF THE RESULTS OF GEOLOGICAL AND TECHNICAL MEASURES TAKEN INTO ACCOUNT OF THE GATHERING SYSTEM

This article provides a rationale for the method of «express» assessment of changes in well flow rate when performing geological and technical measures in an oil field. In view of the cluster configuration of wells that is widespread in Western Siberia, the author identifies two groups of well pads, one of which is influencing, the other is reacting. At the influencing well clusters, as a result of the implementation of geological and technical measures to increase extraction from the productive formation, an increase in the produced fluid occurs. This leads to destabilization of the equilibrium in the “reservoir – well – pipeline” system. The consequence of a violation of this balance is an increase in the load on the reservoir fluid pumped in oil and gas gathering pipelines on individual branches of the field collection system. This leads to a redistribution of wellhead pressures at production wells, which, in turn, also leads to a change in bottomhole pressure and a change in well productivity. The author defines well pads where there is no increase in production, but where there is a change in pressure at the wellheads as reactive. With an increase in linear pressure on the pads, the energy balance in the operating production wells at the reacting well pads shifts towards a decrease in flow rate. Conversely, when wellhead pressure decreases, well productivity increases. These parameters will change until the energy balance of pressures in the wells is achieved. In connection with the above, within the framework of this work, an approach is presented for assessing changes in well operating parameters with increasing production in the field. An example of testing the proposed approach on a complex integrated model is given. To test this approach for adequacy, a comparison was made of theoretical calculations of changes in the operation of real wells with calculations performed on a customized complex integrated model of an oil field. It is worth noting that this type of calculation is characterized as approximate. Its use is advisable exclusively at the stage of preparing programs for performing geological and technical measures to determine reservoir zones in which the negative effect of increasing production will be minimal.

Prediction and Application of Drilling-Induced Fracture Occurrences under Different Stress Regimes

Identifying and categorizing drilling-induced fractures is pivotal for understanding the mechanisms underlying wellbore instability, drilling fluid loss, and assessing reservoirs using imaging logging data. This study employs a linear elastic stress model around the wellbore, coupled with a tensile failure criterion, to establish a predictive framework for the orientation of drilling-induced fractures. It investigates how engineering parameters like wellbore trajectory and bottomhole pressure influence the distribution of principal stresses around the wellbore, as well as the angle and orientation of drilling-induced fractures relative to the wellbore axis, across various faulting scenarios. The results indicate that drilling-induced fractures exhibit structured arrangements and consistent patterns, often appearing at approximately 180° symmetric intervals and descending in similar orientations. This provides a theoretical basis for their systematic identification and classification. Under different stress conditions, these fractures can manifest as feather-like shapes, “J”-shaped, or transitional states between feather-like and “J”-shaped orientations, as well as “V”-shaped or “M”-shaped orientations. Accurate detection and classification of these fractures are essential for interpreting effective fractures, conducting thorough reservoir evaluations, and predicting appropriate drilling fluid densities to mitigate the wellbore failure risk. Moreover, this knowledge aids in effectively determining the magnitude and direction of in situ stress inversion.

An Improved Artificial Electric Field Algorithm for Determining the Maximum Length of Gravel Packing in Deep-Water Horizontal Well

Gravel packing in deep-water horizontal wells is an effective and practical sand control method, which is a key technical method to ensure efficient exploitation of deep-water oil and gas. To ensure the successful implementation of gravel packing in deep water horizontal wells, it is crucial to carry out effective optimization design of packing parameters. This paper proposes a novel optimization design approach for gravel packing in deep-water horizontal wells. In the proposed approach, an optimization model is proposed for gravel packing in deep-water horizontal wells, in which the gravel packing length is regarded as the objective function. Then, an improved artificial electric field algorithm (IAEFA) is introduced for optimizing the key gravel packing parameters so as to determine the maximum gravel packing length. For a specific case study, we conducted optimization calculations for gravel packing in a deep-water horizontal well. Results of the case study demonstrate that the optimization design approach based on the IAEFA algorithm can effectively address the parameter optimization problem of deep-water horizontal well gravel packing. For the target well of the case study, the maximum packing length obtained by the IAEFA algorithm could reach 1000.22 m, and the corresponding 3 sets of optimal packing parameters were also obtained. In the scenario of optimal packing parameters, the total time of gravel packing in target well is 566.6 min, and the total amount of sand consumption is 54,050.94 lbs. The bottom hole pressure during the injection stage remains stable with about 9780 psi, then slowly rises from 9788 psi to 9837 psi in the α-wave packing stage, and rapidly increases from 9837 psi to 9986 psi in the β-wave packing stage. The proposed approach provides an efficient and practical optimization tool for the optimal design of gravel packing in deep water horizontal wells.

Automated Workflow Uses Pressure, Rate Measurements for Well Monitoring

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218470, “A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements,” by Anton Shchipanov, SPE, NORCE; Boyu Cui, SPE, University of Stavanger; and Vitaly Starikov, SPE, Heriot-Watt University, et al. The paper has not been peer reviewed. _ The complete paper describes an integrated workflow for automated well monitoring using pressure and rate measurements obtained with permanent gauges and flowmeters. The workflow is based on time-lapse pressure transient analysis (PTA) and integrates the following components: virtual flowmetering (VFM), transient identification, feature extraction and pattern recognition in transient pressure responses, and assessment of well performance based on PTA metrics. The methodology behind the workflow combines different physics-informed and data-driven methods described in the paper. A New Workflow for Automated Well Monitoring The concept of the automated workflow described in the complete paper has been suggested in the literature. The workflow consists of four main steps (Fig. 1), and integrates the following components: - VFM - Pressure transient identification - PTA feature extraction and time-lapse pattern recognition - PTA metrics to obtain the well-performance profile VFM. The well flow rate is often measured more sparsely than its bottomhole or wellhead pressure for many reasons. However, the permanent downhole or wellhead gauges, when installed in a well, provide high-frequency real-time pressure and temperature measurements. This disparity in the pressure- and rate-measurement sampling rates is a problem for PTA, which requires accurate and synchronized input for flow rates in line with the pressure time series. VFM helps reconstruct missing flow-rate measurements using available pressure measurements. The automated well-monitoring workflow described in this paper would benefit from integration of a relatively simple but robust and scalable VFM model having modest input-data requirements. The steady-state vertical-lift-performance-based VFM model was chosen for use by the authors after testing. The model uses only the bottomhole and wellhead pressure difference, which fits well with the automated workflow requirements. Put simply, the VFM-developed algorithm identifies the suitable pressure and flow-rate samples to train the VFM model; estimates, filters, and verifies the evolution of the model coefficients; and predicts the missing flow-rate values using the pressure measurements where available. This prediction applies only to the time when the well is flowing; as for shut-in periods, the algorithm assigns zero rate values. Transient Identification. Proper transient identification is crucial for PTA because accurate identification of the starting break point of a transient and sequential synchronization with corresponding rate governs the reliability of PTA interpretation results. This is important not only for the transient in question but also for the well history preceding the transient. In the latter case, proper transient identification may help in reliable rate time allocation throughout the well history for common cases of uncertain rates. The workflow of the extended method consists of two main stages. First, the breakpoints indicating start and end of shut-in transients are detected using the topographic prominence max rotation (TPMR) method. This automatically provides detection of flowing transients between the shut-ins identified. Second, the breakpoints of multirate periods (assuming stepwise rate changes) are detected within the long flowing transients using the local minimum in rotation (LMIR) method. As a result, the well pressure history is divided into sequential flowing and shut-in transients, also differentiating flowing transients governed by stepwise rate changes. Neither TPMR nor LMIR methods require denoising, so raw data from permanent gauges may be used without any preprocessing.

Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models

To achieve carbon dioxide (CO2) storage through enhanced oil recovery, accurate forecasting of CO2 subsurface storage and cumulative oil production is essential. This study develops hybrid predictive models for the determination of CO2 storage mass and cumulative oil production in unconventional reservoirs. It does so with two multi-layer perceptron neural networks (MLPNN) and a least-squares support vector machine (LSSVM), hybridized with grey wolf optimization (GWO) and/or particle swarm optimization (PSO). Large, simulated datasets were divided into training (70%) and testing (30%) groups, with normalization applied to both groups. Mahalanobis distance identifies/eliminates outliers in the training subset only. A non-dominated sorting genetic algorithm (NSGA-II) combined with LSSVM selected seven influential features from the nine available input parameters: reservoir depth, porosity, permeability, thickness, bottom-hole pressure, area, CO2 injection rate, residual oil saturation to gas flooding, and residual oil saturation to water flooding. Predictive models were developed and tested, with performance evaluated with an overfitting index (OFI), scoring analysis, and partial dependence plots (PDP), during training and independent testing to enhance model focus and effectiveness. The LSSVM-GWO model generated the lowest root mean square error (RMSE) values (0.4052 MMT for CO2 storage and 9.7392 MMbbl for cumulative oil production) in the training group. That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group (RMSE of 0.6224 MMT for CO2 storage and 12.5143 MMbbl for cumulative oil production). PDP analysis revealed that the input features “area” and “porosity” had the most influence on the LSSVM-GWO model's prediction performance. This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO2 subsurface storage and cumulative oil production. It also establishes a new standard for such forecasting, which can lead to the development of more effective and sustainable solutions for oil recovery.

Fast Optimization of the Net Present Value of Unconventional Wells Using Rapid Rate-Transient Analysis

Summary Decline-curve analysis (DCA) is the industry standard to predict hydrocarbon production to then estimate the net present value (NPV) of unconventional wells. Conventional DCA assumes the flowing bottomhole pressure (BHP) is constant. This is an unrealistic assumption for many unconventional wells that can lead to incorrect estimates of ultimate recovery and thus erroneous NPV calculations. This work illustrates the application of a novel technique that combines variable BHP conditions with decline-curve models [Rapid rate-transient analysis (RTA)] to estimate the NPV and select the optimal cluster spacing and number of fractures for a given well. We compare the results of DCA and Rapid RTA to estimate and optimize the cluster spacing for a tight oil and shale gas well. The Rapid RTA results show marked differences in terms of the shape and the optimal value of the NPV function from the simpler DCA method. For the two cases illustrated, DCA results are a direct consequence of the rate-time analysis failing to correctly detect the onset of boundary-dominated flow (BDF). In contrast, Rapid RTA correctly detects the onset of BDF and presents a defined maximum value of the NPV vs. cluster spacing (number of fractures) function. This optimal value is a trade-off between the fracture treatment cost, the speed of recovery of hydrocarbons, and the value of money with time (discount rate). The major contribution of this work is the implementation of the Rapid RTA technique allowing fast computation of pressure-rate-time analysis and NPV calculations with computational times comparable with DCA for optimizing fracture cluster spacing and the total number of fractures of unconventional wells. We have developed a web-based application to give readers a hands-on experience of this new technique and to analyze and optimize the NPV of unconventional wells.