Dynamic Production Data Research Articles

Quantitative evaluation of the carbonate reservoir heterogeneity based on production dynamic data: A case study from Cretaceous Mishrif formation in Halfaya oilfield, Iraq

Reservoir heterogeneity greatly affects the reservoir production ability. The heterogeneity in a carbonate reservoir is extremely serious. To provide an accurate description of the reservoir production ability, it is essential to quantitatively characterize and evaluate the carbonate reservoir heterogeneity. However, the evaluation criteria of carbonate reservoir heterogeneity are still unclear and hard to reflect the effect of the reservoir heterogeneity on oil and gas production. In this study, we developed an approach to quantitatively evaluate both the intermediate heterogeneity (i.e., the heterogeneity in a layer) and interlayer heterogeneity in a carbonate reservoir by comparing the heterogeneity characterization parameters with dynamic production data. The developed evaluation criteria can reflect the effect of the reservoir heterogeneity on oil and gas production and can be readily adopted in oilfield. The heterogeneity characterization parameters used are the permeability variation coefficient, permeability dart coefficient, and permeability contrast coefficient, respectively. The dynamic production data are required to be firstly split to obtain the production rate of a single layer. A case study from Mishrif formation in Halfaya oilfield is conducted. Based on the newly developed method, the intermediate heterogeneity of the study region is divided into three types, i.e., the homogeneous, weak heterogeneous, and strong heterogeneous types. While the interlayer heterogeneity is divided into two types, i.e., the weak and strong heterogeneous types. In this case study, the permeability variation coefficient is the best choice to describe the effect of heterogeneity on oil production among the three heterogeneity characterization parameters. Therefore, the planar distributions of intermediate heterogeneity for each small layer and that of interlayer heterogeneity for Mishrif formation in the study region are generated based on the permeability variation coefficient. Based on the developed evaluation criteria, we can find that the intermediate heterogeneity of most layers belongs to the weak and strong heterogeneous types. Moreover, the interlayer heterogeneity of the study region belongs to the strong heterogeneous type. By comparing the distributions of reservoir heterogeneity and sedimentary microfacies, we can conclude that the reservoir heterogeneity is strong in the area where multiple sedimentary microfacies exist, while it is weak in the area controlled by a single sedimentary microfacies. • An approach to quantitatively evaluate both the intermediate heterogeneity and interlayer heterogeneity in a carbonate reservoir is developed by comparing the heterogeneity characterization parameters with dynamic production data. • The developed evaluation criteria can reflect the effect of the reservoir heterogeneity on oil and gas production and can be readily adopted in oilfield. • The reservoir heterogeneity is strong in the area where multiple sedimentary microfacies exist, while it is weak in the area controlled by a single sedimentary microfacies.

Reservoir production optimization based on surrograte model and differential evolution algorithm

Production Optimization is a significant method for oilfields to control water cut and stabilize oil production. When the oilfield enters the high or ultra-high water cut stage, it becomes particularly important to use production optimization methods for improving the water-flooding efficiency. Currently, the commonly used production optimization methods are based on reservoir simulators. Such methods require lots of forward simulations during optimizing, which results in low computational efficiency. It's not applicable to the reservoir without numerical simulation models. Thus, a new production optimization method based on the reservoir proxy model is proposed in this work. Firstly, the dynamic production data of the oilfield are collected and preprocessed for training the Extreme Gradient Boosting model, and constructing the proxy model for water cut prediction of producers and the reservoir. Then, an optimal control model for minimizing the water cut of the reservoir can be constructed based on the proxy model. Finally, an optimal injection-production scheme can be obtained by using the differential evolution algorithm. For the evaluation and verification purposes, the proposed method is applied to a well block from SL oilfield, China. Empirical results demonstrated that the proposed method can effectively improve the water-flooding efficiency. • A proxy model based production optimization framework was proposed in this work. • The XGBoost based proxy model was established to predict the water cut of wells. • DE is introduced to solve the optimal model constructed by proxy and constraints. • The proposed method effectively optimize and improve the water-flooding efficiency.

Artificial Neural Network- (ANN-) Based Proxy Model for Fast Performances’ Forecast and Inverse Schedule Design of Steam-Flooding Reservoirs

Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

A data-driven shale gas production forecasting method based on the multi-objective random forest regression

Shale gas is an important unconventional natural gas resource existing in shale reservoir with huge reserves. Due to the ultralow porosity and permeability, it requires the horizontal well drilling and the multi-stage hydraulic fracturing technology to successfully produce the shale gas. The accurate prediction of shale gas production is crucial to the reasonable design of the development plan. However, due to the complex hydraulic fracture network and the gas flow mechanism, the physics-based shale gas production prediction model is still under way. The data-driven model provide an alternative way to deal with the production prediction problem. The multi-objective random forest method is proposed to predict the dynamic production data. The geological and hydraulic fracturing properties are used as input feature. Its prediction performance is evaluated based on the R squared values after determining the appropriate hyper-parameters. The ranking of variable importance can be helpful to improve the interpretability of the data-driven model. The initial peak production rate before declining can be also used as an additional input feature. With the initial peak production rate augmented into the feature set, it can greatly improve the prediction of shale gas production. The variable importance analysis results show that it can be the most influencing factor to prediction accuracy and the ranking of other factors can be altered significantly. The performance of multi-objective random forest (MORF) and multi-output regression chain (MORC) methods are compared, and the comparison result indicates MORC requires a relatively smaller random forest structure, but the prediction performance of MORF is better than MORC. More sample data with less measurement errors can increase the accuracy of the data-driven shale gas production model but there exists a threshold value to improve the accuracy gain.

Study on Well Selection Method for Refracturing Horizontal Wells in Tight Reservoirs

Refracturing technology is one of the key technologies to recover the productivity of horizontal wells in tight oil reservoirs, and the selection of best candidate wells from target blocks is the basis of this technology. Based on the refracturing production database, this paper analyzes the direct relationship between geological data, initial fracturing completion data, and dynamic production data, and the stimulation effect of refracturing. Considering the interaction among multiple factors, the factors affecting the stimulation effect of refracturing are classified and integrated, and a comprehensive index including geology, engineering, and production is constructed, making this index meaningful both for physical and engineering properties. The XGBoost decision tree model is established to analyze the weight of influence for the comprehensive index of geology, engineering, and production in predicting the stimulation effect of refracturing. A comprehensive decision index of refracturing well selection is formed by combining the above three for performing a fast selection of horizontal candidate wells for fracturing. Taking a horizontal well test area in Songliao Basin as an example, the target wells of refracturing are selected by this method, and field operation is carried out, and a good stimulation effect is achieved. The results show that the comprehensive decision-making index constructed by this method is reliable and has certain guiding significance for well selection and stimulation potential evaluation of tight oil reservoir.

Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods

With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. A numerical simulation technique was adopted to obtain the required dataset, which contains dynamic production data calculated under different heterogeneous reservoir conditions. Machine learning models were established through deep neural networks, which learn and capture the characteristics better between dynamic production data and reservoir heterogeneity, so as to invert the vertical permeability. On the basis of model validation, the results show that machine learning methods have excellent performance in predicting heterogeneity with the RMSE of 12.71 mD, which effectively estimated the permeability of the entire reservoir. Moreover, the overall AARD of the predictive result obtained by the CNN method was controlled at 11.51%, revealing the highest accuracy compared with BP and LSTM neural networks. And the permeability contrast, an important parameter to characterize heterogeneity, can be predicted precisely as well, with a derivation of below 10%. This study proposed a potential for vertical heterogeneity prediction in reservoir basing on machine learning methods.

Artificial neural network–based time-domain interwell tracer testing for ultralow-permeability fractured reservoirs

Affected by water injection, the interwell parameters of injection-production wells in ultralow-permeability fractured reservoirs are changed in the time domain, and interwell tracer testing can characterize interwell characteristics quantitatively. For an oil field in the middle and late stages of development, the number of interwell tracers is reduced to control costs, meaning that some injection-production well groups cannot be monitored effectively. Given the rapid development of artificial intelligence technology by which the complex implicit relationship of parameters can be obtained through sample learning, it is possible that future interwell parameters can be predicted by combining a large accumulation of data from interwell tracer testing of well groups with historical production data. This work took a shallow, ultralow-permeability, fractured reservoir in the eastern part of the Ordos basin as its object, and based on the interpreted parameters of previous interwell tracer tests, combined with the dynamic production data and static geological parameters, an analysis method of time-domain interwell tracer testing was developed for ultralow-permeability fractured reservoirs using an artificial neural network. Learning from actual field samples and analyzing two schemes of neural network structure, the minimum error was found for the scheme that predicted the next interwell parameter based on the interpreted parameters of the last time the tracer reached the minimum, with a relative error of 1.94%. The proposed method can predict future interwell parameters reliably and can provide a new low-cost monitoring method for the middle and late stages of water injection development in ultralow-permeability fractured reservoirs.

Potential for Evaluation of Interwell Connectivity under the Effect of Intraformational Bed in Reservoirs Utilizing Machine Learning Methods

Machine learning method has gradually become an important and effective method to analyze reservoir parameters in reservoir numerical simulation. This paper provides a machine learning method to evaluate the connectivity between injection and production wells controlled by interlayer in reservoir. In this paper, Back Propagation (BP) and Convolutional Neural Networks (CNNs) are used to train the dynamic data with the influence of interlayer control connectivity in the reservoir layer as the training model. The dataset is trained with dynamic production data under different permeability, interlayer dip angle, and injection pressure. The connectivity is calculated by using the deep learning model, and the connectivity factor K is defined. The results show that compared with BP, CNN has better performance in connectivity, average absolute relative deviation (AARD) below 10.01% higher. Moreover, CNN prediction results are close to the traditional methods. This paper provides new insights and methods to evaluate the interwell connectivity in conventional or unconventional reservoirs.

Physical parameter modeling of carbonate dissolution-vug reservoirs based on porosity inversion with optimization algorithm

Building a 3D porosity model for the carbonate reservoir is crucial for the planning of hydrocarbon development. However, properly estimating the porosity of carbonate reservoir using wireline logs is still a challenge task due to the complexity of vugs. We treat the porosity estimate of carbonate as an optimization problem and propose to estimate the porosity of carbonate reservoir by integrating several kinds of petrophysics data. We begin with establishing a series of linear equations between the porosity and wireline logs. The objective is computing a porosity value that simultaneously minimizes the defined linear equations. The multi-functions minimization can successfully improve the robust of the proposed method to noise and the “small” wireline log values due to the complex dissolution vug reservoirs. We build 3D porosity model by applying the cooperative kriging stochastic simulation algorithm to the porosity estimated at well bore location and inverted P-impedance from seismic data. We demonstrate the proposed workflow by applying it to the Ordovician carbonate dissolution-vug reservoir in the L area of Tahe oilfield. The statistical analysis shows that the built 3D model has a high correlation with the dynamic production data of the reservoir.

Improved capacitance model considering bottom-hole flowing pressure and interference between oil wells

With the continuous production of oil wells, the reservoir properties, such as permeability and porosity, are changing accordingly, and the reservoir heterogeneity is also enhanced. This development is vulnerable to the problem of the one-way advance of injected water and low efficiency of water flooding. The interwell connectivity between injection and production wells controls the flow capacity of the subsurface fluid. Therefore, the analysis of interwell connectivity helps to identify the flow direction of injected water, which is of great significance for guiding the profile control and water plugging in the later stage of the oilfield. In this study, based on the principle of mass conservation, a capacitance model considering the bottom-hole flowing pressure was established and solved by using the production dynamic data of injection–production wells. Then, the validity of the capacitance model was verified by numerical simulation, and the influences of well spacing, compression coefficient, frequent switching wells, injection speed, and bottom-hole flowing pressure on interwell connectivity were eliminated. Finally, a practical mine technique for inversion of connectivity between wells using dynamic data was developed. The advantage of this model is that the production dynamic data used in the modeling process are easy to obtain. It overcomes the shortcomings of previous models and has a wider range of applications. It can provide a theoretical basis for the formulation of profile control and water-plugging schemes in the high-water-cut period.

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in the petroleum industry. This investigation proposes an alternative way for evaluating interwell connectivity in oil fields utilizing machine learning. In this study, three-dimensional convolutional neural network (CNN) was utilized to establish a deep learning model, which can invert interwell connectivity combining with dynamic production data. Different from traditional methods that try to construct mathematical formulas to calculate the connectivity among wells basing on physical laws, deep learning model can capture autonomously the changing characteristics of dynamic production data by training continuously and provide a potential to characterize the interwell connectivity accurately without physical model. At the same time, the back propagation (BP) neural network has also been built to analyze the prediction performance, which are compared with CNN. The results demonstrate that CNN has better performance in predicting the connectivity with the overall AARD below 15.35%. Moreover, the connectivity predicted by CNN is closest to the real connectivity factor compared with some traditional methods. The evaluation method on interwell connectivity proposed by this paper provides effective guidance for the secondary development of both conventional and unconventional reservoirs.

Fracture inference and optimal well placement using a multiscale history matching in a HPHT tight gas reservoir, Tarim Basin, China

Fractures play an important role in well placement by influencing the well productivity and dominating the fluid flow underground. Though seismic data is often used to identify fracture swarms, the conductivities of fractures can be hard to evaluate, and data quality of seismic surveys typically decreases as the reservoir becomes deeper. In terms of inferring complex fracture patterns, dynamic production data integration can play a vital role. This paper presents a hierarchical multi-scale history matching approach that combines evolutionary algorithm and streamline method to calibrate fracture permeabilities in a HPHT tight gas reservoir using dual porosity models. The reservoir is located in the Tarim basin, China, and has a depth of more than 7500 m with high pressure (18000 psi) and high temperature (340 °F). The fracture properties of the dual porosity model are initially derived from seismic attributes and further calibrated with dynamic data using the proposed multi-scale history matching. The calibrated fracture model can detect the fracture swarm locations underground. The streamlines generated from the history matched model in conjunction with reservoir properties are used to define a ‘depletion capacity map’ which is then used for optimal infill well placement. Most of the previous streamline-based field applications are limited to incompressible or slightly compressible flow. In this paper streamline-based analytical sensitivities are extended to highly compressible flow. To our knowledge, this is the first-time streamlines have been used to facilitate history matching and optimal well placement for gas reservoirs.

Research on Control Factors of Remaining Oil Distribution

Aiming at the characteristics of the overall dispersion and local enrichment of residual oil in the high water cut period of the continental water flooding reservoir, the Fuyang oil layer in the V block of Xinli Oilfield is taken as the research object, the remaining oil enrichment law is the main line, and the comprehensive application of earthquake, logging. Based on drilling, logging, closed coring, testing and production dynamic data, we conducted research on the effects of various geological factors and development factors on the remaining oil distribution. Preliminary research indicates that the remaining recoverable reserves with a certain scale are mainly controlled by the heterogeneity of reservoirs such as low-order faults, interlayers and physical properties, and the differences in hydrodynamic conditions.

Management and instant query of distributed oil and gas production dynamic data

The multidimensional analysis engine data management platform is constructed using big data distributed storage and parallel computing, data warehouse modeling technology, realizing the optimal management and instant query of distributed oil and gas production dynamic big data. The centralized management and quick response of the production data of more than 36×104 oil, gas and water wells is realized. Multidimensional analysis subject model of oil, gas and water well production is built to pretreat the relevant data. At the level of China National Petroleum Corporation (CNPC), the rapid analysis and applications such as oil and gas production tracking, early production warning of key oilfields, analysis of low production wells and long shutdown wells, classification of reservoir development laws have been realized, and the processing time has been shortened from 1 d to 5 s. The basic unit of oil and gas production analysis is refined from oilfield to single well, making the production management more detailed. The process can be traced step by step according to CNPC, oil field company, field, block and single well, and the oil and gas production performance of each unit can be mastered in real time.

A New Fracability Evaluation Approach for Shale Reservoirs Based on Multivariate Analysis: A Case Study in Zhaotong Shale Gas Demonstration Zone in Sichuan, China

Fracability characterizes the effectiveness of hydraulic fracturing. The existing assessment methods cannot reflect the actual value of the effectiveness due to a lack of comprehensive consideration and neglect of the influences of engineering factors. This study aims to solve this problem by implementing geological static data and production dynamic data in multivariate analysis in Zhaotong shale gas demonstration zone. First, the reservoir quality index (RQI) was introduced to evaluate the exploration potential by integrating the geological parameters with gray relational analysis. Moreover, the differences in fracturing fluid types and proppant sizes were considered, and the operating parameters were normalized on the basis of the equivalence principle. Finally, the general reservoir fracability index (GRFI) was proposed based on a dimensioned processing of the various parameters. A case study was conducted to verify the accuracy and feasibility of this new approach. The results demonstrate that (1) the organic carbon and gas content are adjusted to contribute the most to the calculation of the RQI, while the effective porosity contributes the least; (2) the fracturing scale is the main operating parameter determining the fracability, which has the strongest correlation with the effectiveness of fracking; and (3) the GRFI has a positive correlation with shale gas production, and the lower limit of the GRFI of 2,000 corresponds to a daily production of 50,000 m3/d; this value is defined as the threshold value of a stripper well. The GRFI is consistent with the productivity trend of shale gas wells in the research block, which suggests that the new model is accurate and practical for well candidate selection.

Potential analysis of class II reservoirs after water flooding based on core

With the deepening of oilfield development, residual oil distribution has become an important reference for evaluation of oilfield development effect and selection of measures to improve oil recovery. Based on core data of coring wells, through core analysis and comparison between coring wells of different layers in single well and different coring wells in adjacent blocks at the same layer and different coring wells, combined with sedimentary facies zones and dynamic production and development data, this paper analyses and studies the exploitation situation of class II reservoir a II 1-9 in western S oilfield, in order to provide basis for searching for potential remaining oil and formulate more suitable potential tapping measures.

New calculation method for condensate oil saturation of gas condensate reservoir

ABSTRACT During the cyclic gas injection of gas condensate reservoir with bottom or edge water, complicate phase changes and energy conversion take place. A new calculation model for condensate oil saturation is established based on the principle of volume conservation. Moreover, the calculation model considers the influencing factors such as retrograde condensation, connate water evaporation, elastic expansion of reservoir rocks, and external dynamic water invasion. Combining the reservoir fluid property and dynamic production data, this model can calculate the condensate oil saturation under different reservoir pressures quickly. The Case study shows that the condensate oil saturation obtained by this model coincides with the indoor depletion experiment and reservoir numerical simulation. Meanwhile, the sensitivity evaluation of the above influencing factors is carried out. Furthermore, the factors impacting the calculated results from high to low are external dynamic water invasion, elastic expansion of reservoir rocks and connate water evaporation.

An Integrated Approach for History Matching of Multiscale-Fracture Reservoirs

Summary Naturally or hydraulically fractured reservoirs usually contain fractures at various scales. Among these fractures, large-scale fractures might strongly affect fluid flow, making them essential for production behavior. Areas with densely populated small-scale fractures might also affect the flow capacity of the region and contribute to production. However, because of limited information, locating each small-scale fracture individually is impossible. The coexistence of different fracture scales also constitutes a great challenge for history matching. In this work, an integrated approach is proposed to inverse model multiscale fractures hierarchically using dynamic production data. In the proposed method, a hybrid of an embedded discrete fracture model (EDFM) and a dual-porosity/dual-permeability (DPDP) model is devised to parameterize multiscale fractures. The large-scale fractures are explicitly modeled by EDFM with Hough-transform-based parameterization to maintain their geometrical details. For the area with densely populated small-scale fractures, a truncated Gaussian field is applied to capture its spatial distribution, and then the DPDP model is used to model this fracture area. After the parameterization, an iterative history-matching method is used to inversely model the flow in a fractured reservoir. Several synthetic cases, including one case with single-scale fractures and three cases with multiscale fractures, are designed to test the performance of the proposed approach.

Researches on calculation methods of aquifer influx for gas reservoirs with aquifer support

Abstract The existence of aquifers in gas reservoirs has important influences on the development of gas wells. Water will invade into gas reservoirs when formation pressure decreases during the production of a gas well and then change the well production characteristics severely. It is very important to research the aquifer invasion mechanisms and evaluate the aquifer influx for good knowing and exploring the gas reservoir. In this paper, we have conducted three methods to research the aquifer influx characteristics by evaluating the value of aquifer influx and aquifer influx rate that vary with the production time. Three methods are the Blasingame method, the FMB (Flowing Material Balance) method and the MBE (Material Balance Equation) method. Based on the Blasingame conventional type-curves, the normalized well production data of a gas well influenced by aquifers can be divided into three influx periods: no-aquifer, early-aquifer and middle-late-aquifer influx period. The no-aquifer influx period production data are used to determine well initial gas in place and as a base, and the early-aquifer influx period production data are used to obtain aquifer influx and aquifer influx rate quantitatively. The novel three methods presented can elucidate the aquifer influx characteristics clearly and determine aquifer influx rate quantitatively just using well dynamic production data based on the Fetkovich model. Meanwhile, a simple numerical simulation model was built to verify the three methods, which shows that these three methods are reliable and really have strong practicability in developing gas field reservoirs with aquifer support.

A New Design Method of Gel dosage for Profile Control and Water Plugging

At present, there is no quantitative calculation method of gel plugging agent for profile control and water plugging. Considering the influence of gel plugging agent entering the hyperosmotic channel on the effect radius of profile control and water plugging, and the influence of the dosage requirement for the actual reservoir water storage and flooding area, a new method for quantifying dosage calculation of gel plugging agent was put forward based on the dynamic production data. According to the average dosages of Q1, Q2, Q3 calculated respectively based on the displacement radius method of well testing, the water drive method of formation water storage, and the displacement radius method of advantage channels, the dosage of blocking agent Qt for profile control and the dosage of blocking agent Qd for water plugging were obtained. The method can meet the actual needs of gel dosage and relieve the layer contradictions and inner contradictions effectively, which will insure the measures of profile control and water plugging achieve the expected blocking effect.