Predict Oil Production Research Articles

Experimental investigation and theoretical modeling of stress-dependent permeability in naturally fractured tight gas reservoir

Stress-dependent permeability (SDP) of naturally fractured tight gas reservoir (NFTGR) has drawn considerable attention in recent decades. The SDP of natural fracture (NF) is quite different from that of the rock matrix (RM) due to the variance NF width and NF filling degree. The SDP of naturally fractures can be obtained by conducting permeability measurement experiments on naturally fractured core plugs (NFCP). However, it is usually impossible to obtain downhole NFCPs due to economic and technical limitations. Our research group obtained various types of naturally fractured downhole core plugs from a NFTGR (Tarim filed, China) and make it possible to investigate the effect of NFs on SDP. The NF types of the downhole core plugs include RM core plug (RMCP), unpenetrated-ring fractured core plug (URFCP), penetrated-vertical fractured core plug (PVFCP), penetrated-incline fractured core plug (PIFCP), unpenetrated-U-type fractured core plug (UUFCP), and multiple fractured core plug (MFCP). We conducted SDP experiments through the application of pressure-pulse-decay (PPD) method. Results showed that the initial permeability of RMCP and MFCP were higher than that of others, and the permeability vanished stress of them were also highest, indicating that the flow resistance of RMCP and MFCP were lower than those of others. It is also shown that the degree of permeability stress-dependent (DPSD) varied from NF types. A modified exponential permeability model was derived to evaluate DPSD of natural fractured core plugs. Two parameters (γσeff and γσeff') were used to describe the DSDP in NFTGR, and the range of which were obtained from the experimental data by regression analysis. The reasonable γσeff and γσeff' range of NFCP were also proposed in this paper. Sensitivity analysis showed that the influence γσeff' on SDP was more significant than that of γσeff. The proposed model can be integrated to reservoir simulators and help to improve of oil and gas production prediction accuracy in NFTGRs.

A new approach to water cut forecasting based on results of capacitance resistance modeling

For oil fields that are at a late stage of development, urgent tasks are the operational analysis of the development and optimization of the operating modes of injection wells. The demand for responsiveness often forces one to abandon the use of three-dimensional hydrodynamic models in favor of analytical ones such as CRMP. Using CRMP models allows you to quickly assess the trends in the impact of injection wells on producing wells and build reliable short-term forecasts for fluid production. Supplementing the traditional (single-phase) CRMP model with a water cut model also allows predicting oil production rates for producing wells and expands the capabilities of an operational analysis of the existing development system. In addition, an adequate water cut model allows using the CRMP model to solve the problem of optimizing the operating modes of the injection well stock. This article discusses the main known water cut models used in conjunction with the CRMP model, provides a brief analysis of their advantages and disadvantages. A new authorial mathematical model of water cut (“multi-characteristic model”) is proposed, which allows to establish the role of each injection well in changing the water content of the considered producer. An adaptation algorithm is also described, that is, the selection of unknown model coefficients implemented in Ariadna software (developed by Tyumen Petroleum Research Center LLC). The low computational complexity of the algorithm allows you to quickly simulate areas containing up to several hundred wells. The results of experiments on the use of a new mathematical model on a synthetic model of an oil reservoir are presented. The results of predicting water cut are compared with the results of previously known methods. The restrictions for using the new model, as well as directions for its development are indicated.

Dynamic risk assessment of reservoir production using data-driven probabilistic approach

Oil and gas companies use the reservoir simulation approach to history match and forecast reservoir production. While this strategy is easy and often effective, it has inherent setbacks, including a limited ability to capture temporal and spatial variability of the data, uncertainty in the data and reservoir models, and most importantly, impacts of these factors/parameters on the overall production risk profile.This paper presents a hybrid method to forecast the production rate and risks attributed to oilfield development. The hybrid connectionist strategy integrates a data-driven model to capture variability with a probabilistic model to capture uncertainties and associated risks. The multilayer perceptron artificial neural network (ANN) is built on the basis of geological realizations and used as a substitute for the commercial simulator (e.g., CMG) to model reservoir production behaviour for the effective facilitation of the production prediction. The model has a generalization capability and captures the temporal-spatial dependency and non-linear complex relationships. The Bayesian network model is developed to assess the production risks. It employs the concept of the early warning index system. The results show that the predicted oil production profile closely matches the history matched data from the simulator, and the assessed dynamic risks conform with the field reality. The hybrid modeling strategy leads to the minimum and average percentage errors of 0.02% and 5.28% respectively in the reservoir production forecast. The application of the proposed approach would assist in effective reservoir management decision making, enabling a risk-based optimal field performance of reservoir.

Optimization of reasonable production pressure difference of multiple-fractured horizontal well in fractured tight reservoir

There is pressure-sensitive effect in the development of tight reservoirs, so we need to optimize the reasonable production pressure difference. This paper determined the main controlling factors of reasonable production pressure difference and provided guidance for optimal design of production pressure difference. Based on Green’s function and source/sink method, a semi-analytical productivity model of multiple-fractured horizontal well (MFHW) considering the pressure-sensitive effect of artificial fractures and natural fractures is established, which can be used to predict the unstable productivity of MFHWs and analyze the effect of parameters on productivity and reasonable production pressure difference. Modeling results are compared with those from numerical model built by software CMG, obtaining a good match. Afterwards, sensitivity analysis was done in the work, three key parameters are analyzed: (1) pressure-sensitive coefficient of artificial fracture, (2) compressibility of natural fracture network, and (3) direction of principal permeability. The results show that the reasonable production pressure difference is mainly related to the pressure-sensitive coefficient of artificial fractures. By contrast, the difference of compression coefficient between natural fractures and direction of principal permeability has a small impact on reasonable production pressure difference. Finally, the proposed model is used to calculate the reasonable production pressure difference of JHW015 in Xinjiang oilfield and the reasonable production pressure difference of JHW015 is 8.4 MPa. The model considering the pressure-sensitive effect of artificial and natural fractures can predict oil production more accurately, which can provide the important evidences for reasonable production difference pressure and fracturing optimization design in tight reservoir.

A new numerical simulator considering the effect of enhanced liquid on relative permeability

Abstract Enhanced liquid treatment magnifies the difference of fluid flow velocity between borehole and inter-well zones. The flow velocity will influence the relative permeability and change the percolation law in the oil-water two-phase flow. In this study, we conduct three group experiments with different permeability using 15 artificial cores. The unsteady-states measurement method is used to test the relative permeability curve at different velocities. We found that relative permeability would be affected by the testing velocity based on the experimental results. A new relative permeability equation considering flow velocity is established, which indicates that higher oil recovery can be obtained by an early enhanced liquid treatment. There is also a critical velocity with different permeability. If the flow rate (vw) of the enhanced liquid treatment is larger than the critical velocity, the treatment will improve the oil recovery. On the contrary, if the vw of the enhanced liquid treatment is larger than the critical velocity, the treatment will reduce the oil recovery. We summarize the new relative permeability equation and implement it to the numerical simulation model. The new simulator is validated with the three-parallel sand-pack experiment and the actual China offshore oilfield model. The new simulator could predict oil production accurately after enhanced liquid treatment.

Application Of Comprehensive Evaluation Method In Oil Well Classification

China’s oil resources are very rich, but the effect of oil exploitation is not good. Precise prediction of oil production plays an extremely important role in formulating a reasonable oil planning plan and determining the direction of investment. In order to improve the accuracy of oil well exploitation, it is necessary to establish a scientific oil production forecasting system. First, pre-treatment research on the data of Baikouquan Oilfield to ensure the quality of oilfield data. Secondly, the main controlling factors affecting the production of oil wells are analyzed, and the single evaluation is carried out by the coefficient of variation method, the entropy method and the mean square error method. Then, the pre-examination test of the consistency test is performed on the single evaluation result, and the combination of the evaluation methods is selected. The combined evaluation method is the average method, and the fuzzy Borda method. It is again tested whether the combined evaluation result has passed the consistency test. After the consistency test, the sum of the squares of the errors of the three combined evaluations is calculated, and the optimal one is optimized. The combined evaluation method was used for the second combination evaluation, and the results of all the evaluation methods were compared to observe whether the combined evaluation results were effective, and 8 main control factors were analyzed and screened. Finally, the main control factor data analyzed by comprehensive evaluation is used for discriminant analysis, and the fitting effect is compared. It is found that the fitting results are basically consistent with the data of the oilfield site, indicating that the results of the main control factors analyzed by the comprehensive evaluation are better. The prediction accuracy meets the engineering requirements and is of great significance for the prediction of on-site oilfield production.

New SAGD model for oil production using a concave parabola steam chamber geometry

In this paper, a new steam chamber growth model using a concave parabola interface is presented. Steam chamber shape is crucial in describing the expansion of injected steam and the propagation pattern of the temperature of the steam front towards the producing well. Steam chamber evolution is particularly important for predicting oil production during steam assisted gravity drainage (SAGD) processes.The new model is derived based on the orientation of the temperature isoline obtained for the underground test facility (UTF) project, the results of several experiments, and the numerical simulation of SAGD processes for major field projects. The analytical description of this steam chamber growth pattern will assist in evaluating and developing fields using SAGD scheme. The model employs the concept of heat conduction involving a moving solid to estimate the steam chamber interface temperature profile and, eventually, the oil production rate and associated steam injection rate required based on an energy balance approach. This study builds upon the assumptions of Butler and Reis’ linear interface by introducing a parabolic steam interface. It is demonstrated that the new model predicts oil production and steam injection rates with higher accuracy than existing models. It also showed the strong variation of oil rate with time unlike the constant rate assumption. The results of the model are compared with the experimental data from Cheng and Butler, simulation with a CMG STARS simulator and the results of the UTF phase B project, which show better agreement and robustness.

A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production

In the process of oilfield development, it is important to predict the oil and gas production. The predicted value of oil production is the amount of oil that may be obtained within a certain area over a certain period. Because of the current demand for oil and gas production prediction, a prediction model using a multi-input convolutional neural network based on AlexNet is proposed in this paper. The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively. It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance.

Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

None of the various published models used to predict oil production rates through wellhead chokes from fluid composition and pressures can be considered as a universal model for all regions. Here, a model is provided to predict liquid production-flow rates for the Reshadat oil field offshore southwest Iran, applying a customized genetic optimization algorithm (GA) and standard Excel Solver non-linear and evolutionary optimization algorithms. The dataset of 182 records of wellhead choke measurements spans liquid flow rates from < 100 to 30,000 stock tank barrels/day. Each data record includes measurements of five variables: liquid production rate (QL), wellhead pressure, choke size, basic sediment and water, and gas–liquid ratio. 70% of the dataset (127 data records) was used for training purposes to establish the prediction relationships, and 30% of the dataset (55 data records) was utilized for independently testing the accuracy of the derived relationships as predictive tools. The methodology applying either the customized GA or standard Solver optimization algorithms, demonstrates significant improvements in QL-prediction accuracy with the lowest APD (− 7.72 to − 2.89), AAPD (7.33–8.51), SD (288.77–563.85), MSE (91,871–316,429), and RMSE (303.1–562.52); and the highest R2 (greater than 0.997) compared to six previously published liquid flow-rate prediction models. As a general result, the novel methodology is easily applied to other field/reservoir datasets, to achieve rapid practical flow prediction applications, and is consequently of worldwide significance.

Analytical modelling of steam chamber rise stage of Steam-Assisted Gravity Drainage (SAGD) process

When steam is injected continuously in a SAGD operation, a steam chamber starts to develop in three stages; rise, lateral spreading and confinement, as observed in the field and laboratory experiments. The physics of chamber development in the first and the two last stages is different and thus modelled separately. Nearly all of the available theoretical analyses of SAGD are concerned with the lateral spreading stage of the steam chamber development with the exception of a few works which aim to determine rise velocity and oil rate. The rate of the upward growth of the steam chamber has a profound effect on the SAGD performance, because as the chamber reaches the top of the reservoir heat loss starts to play a significant role in the thermal efficiency of the process.During the rise period, as the steam chamber grows upwards, oil drains downwards and so the process at this stage must account for to the frontal instability between steam and the liquid phases in the system. Stability is affected by factors such as the flow direction, gravity, and viscosity difference between gas/steam and liquid phases. Steam condensation, on the other hand, has a stabilizing effect.In the present model, the rise velocity and the steam chamber height were calculated by combining volumetric oil displacement with Darcy oil rate considering the indirect effect of frontal instability. The model is extended to predict oil production, heat or steam injection rate, heat consumption and CSOR during this phase. Moreover, estimates of injected steam sweep efficiency and the angle between the steam chamber and the horizon are achieved. The model results show an increase in rise velocity with temperature and permeability. Also, the calculated oil production rate increases and Cumulative Steam Oil Ratio (CSOR) declines with time. This theory is tested via comparison with several field data sets to show the adequacy of the model.

A new workflow to improve the carbonate reservoir types discrimination combing the empirical model decomposition and energy entropy classification methods

Classification and well-logging evaluation of carbonate reservoir rock is very difficult. On one side, there are many reservoir pore spaces developed in carbonate reservoirs, including large karst caves, dissolved pores, fractures, intergranular dissolved pores, intragranular dissolved pores, and micropores. On the other side, conventional well-logging response characteristics of the various pore systems can be similar, making it difficult to identify the type of pore systems. We have developed a new reservoir rock-type characterization workflow. First, outcrop observations, cores, well logs, and multiscale data were used to clarify the carbonate reservoir types in the Ordovician carbonates of the Tahe Oilfield. Three reservoir rock types were divided based on outcrop, core observation, and thin section analysis. Microscopic and macroscopic characteristics of various rock types and their corresponding well-log responses were evaluated. Second, conventional well-log data were decomposed into multiple band sets of intrinsic mode functions using empirical mode decomposition method. The energy entropy of each log curve was then investigated. Based on the decomposition results, the characteristics of each reservoir type were summarized. Finally, by using the Fisher discriminant, the rock types of the carbonate reservoirs could be identified reliably. Comparing with conventional rock type identification methods based on conventional well-log responses only, the new workflow proposed in this paper can effectively cluster data within each rock types and increase the accuracy of reservoir type-based hydrocarbon production prediction. The workflow was applied to 213 reservoir intervals from 146 wells in the Tahe Oilfield. The results can improve the accuracy of oil-production interval prediction using well logs over conventional methods.

Pore structure and fractal analysis of Lower Carboniferous carbonate reservoirs in the Marsel area, Chu-Sarysu basin

Abstract Evaluation of carbonate heterogeneity, closely related to pore geometry and connectivity, is important in oil and gas field production and reservoir prediction. To profoundly understand the heterogeneity of Lower Carboniferous carbonate reservoirs in the Marsel area, including Visean (C1v) and Serpukhovian (C1sr), the pore structure and fractal characteristics are investigated using routine petrophysical measurements, X-ray diffraction (XRD), cast thin section analysis, scanning electron microscopy (SEM), and mercury injection capillary pressure (MICP) tests. The origin of potentially prolific reservoir, the relationships between fractal dimension and reservoir physical properties, mineral composition and pore structure are also discussed. XRD results reveal that the mineral compositions are highly heterogeneous in C1v and C1sr. Routine petrophysical experiments indicate that C1v and C1sr are generally tight. However, some high porosity-permeability zones exist in C1sr, which is described as a potentially prolific reservoir. The cast thin section and SEM analyses identify three main pore types in the potentially prolific reservoir: interparticle dissolution pores, intercrystalline dissolution pores, and moldic pores. The MICP data reflect that the pore-throat radii in the prolific reservoir range from 0.29 to 4.6 μm, whereas those in tight section are typically

Multivariate statistical applications for quantifying contribution of seismic data to production prediction in the Wolfcamp, Midland Basin

A workflow with the objective of quantifying the added value of seismic-based information to improved prediction of oil production in the Wolfcamp section of the Midland Basin is described. A variety of multivariate statistical algorithms are employed in the workflow: unsupervised and supervised classification, categorical-based nonlinear regression, and spatial regression for well log estimation. Porosity is estimated from elastic inversion volumes within the context of lithotype classification originating with well logs. Porosity feet becomes the primary driver in production prediction. Two parallel analyses are performed. In the first, only well logs are used to predict porosity along all vertical wellbores. As a proxy for the impact of well spacing, different minimum search distances are investigated for porosity estimation error and associated error in prediction of cumulative oil production at six months. The second analysis is similar except, along each wellbore, porosity is extracted from the seismic porosity volume and then used as soft data. These two analysis flows are then applied to horizontal wellbores, which have no original porosity logs, to compare how well each predicts known cumulative production. The incorporation of seismic-based information does show significant error reduction.

A new soft computing‐based approach to predict oil production rate for vapour extraction (VAPEX) process in heavy oil reservoirs

AbstractThere are vast resources of heavy oil and bitumen reservoirs in the Western Canadian Basin. For many of them up to 95 % of reserves still remain in place, and by considering the increase in future energy demand these abundant resources can be considered as potential sources for future years. Recently, solvent‐based heavy oil recovery methods such as vapour extraction (VAPEX) have gained attention due to the potential environmental and economic assets over thermal processes. Due to the complexity of the mechanisms associated with the solvent injection process (i.e. diffusion and gravity drainage processes), such models are incapable of accurately predicting the production rate during the VAPEX process. In this study, the artificial neural networks (ANN) technique is utilized to tackle the limitations that analytical methods encounter while predicting the complex relationships, where there is uncertainty, imprecision, and partial truth. Hence, in the first phase of the research a comprehensive experimental study in two large‐scale, visual rectangular VAPEX models was carried out by utilizing various injection solvents. Based on an extensive literature review and experimental results, the drainage height, solvent type, permeability, porosity, and heavy oil viscosity were considered as the inputs of the model to predict the heavy oil production rate as the output of the model. After trying different training scenarios, it was found that the back‐propagation learning algorithm can be successfully used to predict the ultimate recovery factor after implementing the VAPEX method in the heavy oil system of interest.

Neural Networks for Prediction of Oil Production

Abstract Artificial neural networks are popular tool in oil and gas industry. The objective of this paper is to develop an artificial neural network that predicts oil production in State Oil Company of Azerbaijan Republic (SOCAR). Multilayer perceptron neural network is applied to predict oil production. As a result 99 percent accuracy in training procedure is achieved. The accuracy of the developed model was compared with empirical correlations. Great matching between neural network and actual data has been found.

油气产量预测的GM(1,1)幂模型

油气产量预测的GM(1,1)幂模型

Prediction of oil production based on SVM optimised multi-objective particle swarm optimisation

The unstable availability and capacity exist in energy efficiency optimisation regarding to renewable energy supply for cellular network due to several factors, such as climate change, high oil resource consumption and energy safety etc. The energy supply sustainability problem is modelled as optimisation solution problem aiming at maximising NP-hard network energy residue ratio (ERR). The network ERR maximisation algorithm has been put forward through analysis of relationship between energy efficiency and energy depletion rate (EDR). This algorithm is worked by adopting cross-layer manner, which first maximises the link energy efficiency by the power control at physical (PHY) layer and then maximises network ERR by the access control at media access control (MAC). The simulation results show that the network ERR maximisation algorithm performs excellently in improving network lifetime as well as increasing the number of users served by renewable energy.

Prediction of oil production based on SVM optimised multi-objective particle swarm optimisation

The unstable availability and capacity exist in energy efficiency optimisation regarding to renewable energy supply for cellular network due to several factors, such as climate change, high oil resource consumption and energy safety etc. The energy supply sustainability problem is modelled as optimisation solution problem aiming at maximising NP-hard network energy residue ratio (ERR). The network ERR maximisation algorithm has been put forward through analysis of relationship between energy efficiency and energy depletion rate (EDR). This algorithm is worked by adopting cross-layer manner, which first maximises the link energy efficiency by the power control at physical (PHY) layer and then maximises network ERR by the access control at media access control (MAC). The simulation results show that the network ERR maximisation algorithm performs excellently in improving network lifetime as well as increasing the number of users served by renewable energy.

A Regional Oil Extraction and Consumption Model. Part II: Predicting the Declines in Regional Oil Consumption

In Part I of this analysis, the striking similarities of the declining oil production in the North Sea, Indonesia and Mexico were used to model the future maximum possible oil production per annum in all larger countries and regions of the planet from 2015 to 2050. In Part II, the oil export and oil consumption patterns, that were established in recent decades, are combined with the consequences of the forecast declines in regional oil production that were developed in Part I of this analysis. The results are quantitative predictions of the maximum possible region-by-region oil consumption during the next 20 years. The predictions indicate that several of the larger oil consuming and importing countries and regions will be confronted with the economic consequences of the onset of the world’s final oil supply crisis as early as 2020. In particular, during the next few years a reduction of the average per capita oil consumption of about 5%/year is predicted for most OECD countries in Western Europe, and slightly smaller reductions, about 2–3%/year, is predicted for all other oil importing countries and regions. The consequences of the predicted oil supply crisis are thoroughly at odds with business-as-usual, never-ending-global-growth predictions of oil production and consumption.

Analytical modelling of cyclic steam stimulation (CSS) process with a horizontal well configuration

In this study, a new semi‐analytical model has been developed to model heavy oil production using cyclic steam stimulation (CSS) and horizontal wells. In order to predict oil production, the governing equations of heat and fluid flow in cylindrical coordinate were solved. Average temperature in the formation during the soak period was calculated by solving the transient heat transfer equations. The heat transfer model was then coupled to a fluid inflow model to predict the fluid rates. The developed model is validated with the fine grid numerical simulation of the problem using a commercial numerical simulator. The robustness of the model was tested by comparing the model results with the results from the numerical simulator by changing various parameters such as: steam injection rates, reservoir permeability, relative permeability, oil viscosity, and reservoir thermal diffusivity. With some reasonable assumptions, it was seen that the results from the developed model are very close to the results obtained using the numerical simulator. This paper presents the model development in detail and outlines the steps involved in predicting the performance of a CSS process for a horizontal well configuration.