Years Of Production History Research Articles

Assessment of source-oriented health risk associated with the oral ingestion of heavy metals in dust within an iron/steel smelting-affected area of the North China Plain

Heavy metals (HMs) from iron/steel smelting activities pose notable risks to human health, especially to those living around industrial facilities of North China Plain, the base of China's steel production. In this study, 78 outdoor windowsill dust samples were collected around a large-scale iron/steel smelter with more than 65 years of production history in the western North China Plain. Nine HMs were analysed to comprehensively assess the health risks by integrating Monte Carlo simulation, oral bioaccessibility, and source apportionment. Results showed serious pollution with Cd, Pb, and Zn based on their geo-accumulation index values and concentrations. Four potential sources including industrial sources (49.85%), traffic sources (21.78%), natural sources (20.58%), and coal combustion (7.79%) were quantitatively identified by multivariate statistical analysis. The oral bioaccessibilities of HMs determined by the physiologically based extraction test ranged from 0.02% to 65.16%. Zn, Mn, Cd, and Pb had higher bioaccessibilities than other HMs. After incorporating oral bioavailability adjustments, noncarcinogenic and carcinogenic risks were significantly reduced, especially for adults. The mean hazard index (HI) for children and adults was below the safety threshold (1.0), whereas the mean of the total carcinogenic risk (TCR) based on HM bioaccessibilities in the gastric phase remained above the acceptable level (1.0E-06) (children: 5.20E-06; adults: 1.16E-06). Traffic sources warranted increased concern as it substantially increased TCR. Cd was identified as the priority pollution in iron/steel smelting areas. Assessing source-oriented health risks associated with oral ingestion exposure can guide the management and control of HM contamination within iron/steel smelting-affected areas.

Intelligent Production Prediction of Deep Offshore Hydrocarbon Reservoir: A Case Study of Niger-Delta Region of Nigeria

Current methods for predicting output, such as material balancing and numerical simulation, need years of production history, and the model parameters employed determine how accurate they are. The use of artificial neural network (ANN) technology in the production forecasting of a deep offshore field under water injection/water flooding in Nigeria’s Niger-Delta region is investigated in this study. Oil, water, and gas production rates were predicted using well models and engineering features. Real-world field data from producer and water injection wells in deep offshore is used to test the models’ performance. Ninety percent (90%) of the historical data were utilised for training and validating the model framework before being put to the test with the remaining information. The predictive model takes little data and computation and is capable of estimating fluid production rate with a coefficient of prediction of more than 90%, with simulated results that match real-world data. The discoveries of this work could assist oil and gas businesses in forecasting production rates, determining a well’s estimated ultimate recovery (EUR), and making informed financial and operational decisions.

Nike's Investment Value and Development Trend

With the improvement of people's living standards and the popularity of sports, more and more people associate healthy life with sports, which significantly increases the consumption of sports goods. The United States is the production of sports goods market leader. Nike is a very representative sports goods company, with 110 years of production history. After decades of development, Nike has occupied nearly half of the market share in the high-end sports market in more than 120 countries. This paper selects American Nike company as the research object to analyze its industry environment and competitive strategy. Then, we'll conduct profitability analysis, efficiency ratio analysis, and solvency analysis on Nike and analyze Nike's investment value and development trend in the past three years based on financial indicators. In the research, it is found that Nike has some problems such as weak solvency, high total operating cost and slow turnover of receivables. This paper proposes a solution to the problems in the report.

Naturally Fractured Carbonate Reservoir Characterization: A Case Study of a Mature High-Pour Point Oil Field in Hungary

An integrated technical study was conducted for a field development project in west Hungary. This study offers a better solution for estimating petrophysical properties and fracture facies vertically along the well and laterally for 3D static and dynamic models of naturally fractured reservoirs in carbonate rocks. More than 30 wells with 40 years of production history were used in order to build reliable static and dynamic models. The fracture class/facies plays an essential role in the spatial distribution of petrophysical properties during 3D reservoir modeling. It was defined by integrating conventional logs, image logs, drilling parameters, and production or well test data. Three fracture facies are defined as macrofracture (including permeable subseismic fault), microfracture, and host rock. Subsequently, the fracture class’s spatial distribution is guided by seismic attributes of fault likelihood combined with the geological concept of the fault and damage zone. As a result, the established fracture classes along the wells are validated by static and dynamic subsurface data. A spherical self-organizing map (SOM) was also implemented for predicting the open-fracture location in wells having limited subsurface data. Moreover, fracture lateral distribution follows the distribution of the fault zone of the fault core, high damage zone, low damage zone, and host rock. The higher the fault displacement, the wider the damage zone and fault core formed. Macrofractures and microfractures frequently appear around the fault core and high damage zone. While only microfractures are dominantly present in the low damage zones, in contrast, the unfractured class is dominantly distributed in the host-rock area. Also, the lithologies are considered in distributing the fracture class because the rock mechanic properties and the number of fractures are strongly controlled by rock compositions. Once the fracture class is distributed, porosity, permeability, and water saturation are modeled in the three-dimensional (3D) geocellular model. Finally, this fracture class also plays a role as a rock typing for reservoir simulation. The saturation height model is built using the fracture class distribution resulting in the initialization, history-matching process, and production forecast from 20 wells showing excellent quality. As a novelty, this study offers a better understanding of fracture distribution and accelerates the history-matching process with a more confident result of the production forecast. In the absence of advanced technologies, like image logs and production logging (PLT) measurements, this study still effectively assists us in recognizing the fracture presence and its quality in both well-depth interval and 3D spatial space, and successfully guided us in proposing new infill drilling with strong confidence and delivering on the high end of expected results.

Assessment of factors influencing natural rubber production among smallholder farmers in Liberia using Fuzzy AHP

Natural Rubber (NR) is an important agricultural product utilized in various sectors. NR is one of the world's biggest industries and a critical component in many industries such as automotive, manufacturing, and medicine. Liberia is a top producer of NR with over 127 years of production history. However, since 2014, Liberia's NR net production has decreased by 0.7% year on year and has fallen behind Sri Lanka. NR production in Liberia has experienced scant performance due to many factors. In this project, we employ mixed-method research to examine the factors influencing the decline of the Liberian NR supply chain and propose solutions that will further stimulate the industry's growth. We conduct an MCDM study among 34 smallholder rubber farmers from Grand Bassa, Margibi and Bong counties. Their grading of ten policy objectives and recommendations are programmed using Fuzzy AHP.

Conditioning Model Ensembles to Various Observed Data (Field and Well Level) by Applying Machine Learning Augmented Workflows - Applied to a Mature Field With 70 Years of Production History

Summary Conditioning Model Ensembles to Various Observed Data (Field and Well Level) by Applying Machine Learning Augmented Workflows - Applied to a Mature Field With 70 Years of Production History

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

SummaryForecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data.Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells.The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Normalized Cumulative Production Curves Estimate Ultimate Recovery

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199981, “Use of Normalized Cumulative Production Curves To Estimate Ultimate Recovery of Unconventional Plays in North America,” by Ivan Olea, Hamed Tabatabaie, SPE, and Louis Mattar, SPE, IHS Markit, et al., prepared for the 2020 SPE Canada Unconventional Resources Conference, Calgary, 15-16 September. The paper has not been peer reviewed. Operators and investors are interested in finding better metrics to evaluate the production performance of unconventional multifractured horizontal wells (MFHWs). The complete paper discusses the use of cumulative production ratio curves normalized to a given reference volume in time for different unconventional plays in North America to investigate the median trend for each play and the median ultimate recovery per play. The paper discusses the choice of 12-month cumulative production for a reference volume as a normalization parameter. Introduction Many methods exist for forecasting the production rate from unconventional reservoirs, but all have limitations. Recently, several publications have appeared relating the expected ultimate recovery (EUR) to the initial rate or the cumulative production after 3, 6, or 24 months. In the complete paper, these publications are reviewed, and their learnings extended, to several unconventional reservoirs. Work in 2018 studied 147 MFHWs covering many formations in the Permian Basin and a wide range of input variables and determined EUR using rate transient analysis, numerical simulation, and decline-curve analysis. The authors of that work compared the EUR with various cumulative production intervals (3, 6, 12, and 24 months) and concluded that the correlation with 3 months was poor; 24 months’ cumulative production was an accurate predictor of EUR but was not considered to be an early-enough predictor. That work’s authors chose 12 months as a preferred early-time predictor of EUR and justified its use by stating that operating conditions have usually stabilized by that time. A universal type curve of cumulative production was created as a percent of EUR vs. years of production (e.g., after 1 year of production, 33% of the EUR has been produced). This type curve accounted for different well lengths and completions and both strong and weak wells. The universality of the type curve is consistent with the understanding that the factors that make a well a high- or low-rate producer affect the 12-month cumulative and the EUR proportionally. A different work from 2018 studied approximately 3,000 wells in the Delaware Basin to determine an early indicator of long-term performance. These authors used not only the EUR but also the actual cumulative production from 252 wells with 6 years of production history. Results showed that the correlation coefficient between the 6-year production and various early-time indicators (cumulative production at 30, 60, 90, and 180 days and 1, 2, 3, and 5 years) improves from 0.23 (for 30 days) to 0.73 (for 1 year) and only minimally thereafter. A 2012 work studied public production data from more than 6,000 wells from the Barnett, Fayetteville, and Woodford shales. These authors binned the data into quartiles and used a key performance indicator (KPI) that combines a confusion matrix and a cost matrix (penalty function). Results indicated that the prediction ability of various metrics ranged from 48% (3-month cumulative production) to 85% (2-year cumulative production) and that, in some cases, the peak month production KPI was only marginally lower than the 6- or 12-month cumulative.

Development of Shale Gas Prediction Models for Long-Term Production and Economics Based on Early Production Data in Barnett Reservoir

This study examined the relationship between the early production data and the long-term performance of shale gas wells, including the estimated ultimate recovery (EUR) and economics. The investigated early production data are peak gas production rate, 3-, 6-, 12-, 18-, and 24-month cumulative gas production (CGP). Based on production data analysis of 485 reservoir simulation datasets, CGP at 12 months (CGP_12m) was selected as a key input parameter to predict a long-term shale gas well’s performance in terms of the EUR and net present value (NPV) for a given well. The developed prediction models were then validated using the field production data from 164 wells which have more than 10 years of production history in Barnett Shale, USA. The validation results showed strong correlations between the predicted data and field data. This suggests that the proposed models can predict the shale gas production and economics reliably in Barnett shale area. Only a short history of production (one year) can be used to estimate the EUR and NPV of various production periods for a gas well. Moreover, the proposed prediction models are consistently applied for young wells with short production histories and lack of reservoir and hydraulic fracturing data.

Multiscale modeling of compartmentalized reservoirs using a hybrid clustering-based non-local approach

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach.A novel and rigorous non-local formulation for flow in porous media is presented, in which the reservoir is represented by an adjacency matrix describing the connectivities of comprising compartments. We automatically divide the reservoir into a number of distinct compartments, in which the direction-dependent multiphase flow communication is a function of non-local phase potential differences. Our proposed clustering framework begins with a mixed-type raw dataset which can be categorical/numerical, spatial/temporal, and discrete/continuous. The dataset can contain noisy/missing values of different data types including but not limited to well production/injection history, well location, well type, geological features, PVT measurements, perforation data, etc. Unsupervised clustering techniques suited to the input data types (e.g. k-prototypes, spectral, Gaussian Mixtures, and hierarchical clustering), and appropriate distance measures (such as Euclidean distance, soft dynamic time warping, and mode) are used. The input data is standardized, and upon convergence check, the best clustering representation is obtained. Finally, Support-Vector-Machine technique is utilized in the kernel space to trace a demarcating hyperplane for the clusters.In a specific case study reported here, the proposed workflow is applied to a major field with a couple of hundreds of wells with more than 40 years of production history. Leveraging the fast forward model, an efficient ensemble-based history matching framework is applied to reduce the uncertainty of the global reservoir parameters such as inter-blocks and aquifer-reservoir communications, fault transmissibilities, and block-based oil-in-place. The ensemble of history matched models are then used to provide a probabilistic forecast for different field development scenarios. In addition, the clustering framework enables us to treat missing data and use the augmented dataset for improving the clustering accuracy.In summary, in this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome.

UAE Case Study Highlights Challenges of a Mature Gas-Condensate Field

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 188422, “Well-Integrity Management: Challenges in Extending Life of a Mature Gas-Condensate Field—A Case Study,” by S. Jain, M.A. Al Hamadi, H. Saradva, SPE, and J. Asarpota, Sharjah National Oil Company, and S.J. Sparke, SPE, M. Volkov, and H. Abu Rahmoun, TGT Oilfield Services, prepared for the 2017 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 13–16 November. The paper has not been peer reviewed. Three onshore fields in the Emirate of Sharjah, United Arab Emirates, have more than 30 years of production history from more than 50 gas-condensate wells. While existing resources could be recovered by drilling additional wells, the most economical solution would be to confirm that existing well stock has sufficient well integrity to allow the continued use of these wells safely. Introduction The Sajaa asset consists of three retrograde condensate onshore fields. All wells are naturally flowing, with support from wellhead compression to reduce the effect of liquid loading on these mature gas producers. The reservoir was blown down to produce gas associated with rich condensate, water, and liquefied petroleum gas. With the reservoir’s long production history, its pressure has been greatly reduced. Through use of a highly simplified upstream production technique, the wells continue to produce condensate-rich gas with the low-pressure regime. But, because of the reservoir-pressure decline, the production rate remains reduced. Therefore, surface gas compression is required to reduce backpressure and pump the gas to a nearby production facility for separation and, ultimately, custody transfer and sale. Mature Well Status The asset wells were completed with 5-in. tubing along with a nominal 7-, 9-, and 13-in. completion scheme. For production-optimization purposes, 10% of the wells produce directly up the production casing, while all the wells are completed packerless. The main purpose of the open annulus completion was to avoid the extensive problems and costs encountered previously with packer completions in the hostile environment and allow for chemical corrosion inhibition along the conduit of the A annulus (Fig. 1). The cement behind the casing is generally accepted as accomplishing vertical isolation, though not radial isolation, with the natural pore pressure being applied laterally through the cement as a worst case. Collapse is considered to be more critical as a parameter than burst during production, even more so in mature reservoirs with cemented barriers that support against burst conditions. Actual well-integrity failures in a well stock do not always imply a situation in which uncontrolled flow of fluids reaches the surface. Individual barrier failures in a specific well group depend on a variety of factors, such as geographical area, operator, drilling era, well type, and maintenance quality. Gas is expected to be the primary fluid lost when a multibarrier failure occurs. The Sajaa asset features comprehensive active and passive barrier systems. Cemented-to-surface annuli and heavy-duty wellheads account for the passive barriers; continuous remote monitoring of the annuli and weekly visits to the well location are effective active barriers.

Forecasting production of liquid rich shale (LRS) reservoirs using simple models

Due to the advent of shale reservoirs as important sources of oil and gas production, there has been a critical need for reliable techniques for forecasting production and estimating reserves from these plays. This work presents the results of a study to determine whether simple decline models can be used to forecast production and estimate reserves of both oil and gas in liquid rich shale (LRS) reservoirs with reasonable accuracy. We found that hybrid models (i.e., one model such as the Duong model for transient flow coupled with a different model, such as the Arps hyperbolic model with an appropriate value of the parameter “b”) are appropriate for forecasting oil production and that it is possible to forecast solution gas production with availability of adequate data.To establish the “truth case” for comparison, we simulated production with four different reservoir fluids using a commercial compositional reservoir simulator. We tested a variety of hybrid and simple DCA models on simulated data (and later on field data) with a work flow that included identifying flow regimes with diagnostic plots for each fluid sample. We analyzed 0.5–3 years of production history to estimate model parameters for forecasting future production. Also, we forecasted gas production using a technique similar to one recently presented in the literature.Lengthy transition periods between transient linear flow and boundary dominated flow (BDF) were observed on the diagnostic plots. This is presumably due to multi-phase flow effects, as this transition period is typically much shorter in single-phase shale reservoirs. As in single-phase flow, the Arps decline model proved to be good for forecasting in the BDF regime. For transient flow, we examined the Duong model (which includes transient linear flow as a special case) and YM-SEPD (modified form of Stretched Exponential Production Decline model). In some cases, we applied the Arps model for both the transition period and BDF regimes (with different b values). We concluded that hybrid DCA models are more appropriate for multi-phase flow analysis than simple DCA models. In most of the cases, the hybrid YM-SEPD and Arps models led to more accurate oil production forecasts than other alternatives. Our analyses of solution gas forecasts indicated that solution gas production forecasting is possible but there is still need for more research in this area.

Use of Modern Reservoir Characterization in Mature Fields To Unravel Hidden Reserves

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 177195, “Application of Modern Reservoir Characterization in Mature Fields To Unravel Hidden Reserves,” by M. Rodriguez, F. Abad, L. Rodriguez, and J. Gaibor, Halliburton, and M. Moran and A. Verdezoto, Petroamazonas, prepared for the 2015 SPE Latin American and Caribbean Petroleum Engineering Conference, Quito, Ecuador, 18–20 November. The paper has not been peer reviewed. This paper discusses the successful, fully integrated, 3D Earth modeling (EM) and dynamic reservoir simulation of the Hollin Formation in the Lago Agrio Field within an operator’s first producer field in Ecuador. Early feedback of reservoir heterogeneity helped in understanding key reservoir issues and developing the methods undertaken to solve various problems. The implementation of cutting-edge reservoir-modeling techniques contributed to improving production rates and enhancing recovery. Available Data Lago Agrio has features of a mature field: more than 43 years of production history, 75% of extracted official re-serves P1 and P2 (quantified by the operating company), and a declination from plateau production of 31%, along with a steady decline of the field. New drilling and the workover of existing wells during the reservoir productive life have not been able to increase production. Abundant data exist from a number of wells in the Lago Agrio Field, such as from conventional-log-suite core analysis, pressure/volume/temperature fluid tests, pressure buildup, and a broad production history. Discussion and Results Sedimentological Model. Several authors define three depositional environments for the sediments of the Hollin Formation: fluvial, tidal-dominated, and shallow marine. How-ever, the thickness and lithofacies of these intervals vary from field to field. A sedimentological study was performed with core data, core descriptions, bore-hole well logs, and general knowledge of the Oriente Basin. Petrophysical-Properties Modeling. Diagenetic Analysis. X-ray diffraction and scanning-electron- microscope (SEM) data allow analysis of the mineralogical features in the fluvial, tidal, and marine intervals of the Hollin formation. In the fluvial/tidal Hollin interval, it is observed that quartz is the predominant mineral, followed by clay minerals; calcite and illite minerals were observed toward the top. The most common clay mineral is vermicular kaolinite. Siliceous cement was observed in patches. Toward the top, evidence of calcareous cement is shown. Meanwhile, in the marine Hollin interval, the predominance of glauconite and a significant percentage of ankerite are observed. The existing differential diagenesis between the fluvial/tidal and marine units markedly affects the textural characteristics of the rock and the relationship of flow/storage capacity; therefore, it becomes extremely important to model permeability and rock types (RTs) separately for these units. Porosity Correction. Porosity measurements on core plugs were conducted by various methods and at various pressure confinements. This generated the need to standardize the information before it was used to compare with logs and create models of RTs. In the first in-stance, net confinement pressures were calculated for each reservoir. Then, with the help of the rock- compressibility analysis from a neighboring analog field, the pore volumes were normalized, obtaining porosity changes per each confining-pressure increase. Once core porosity was corrected, the log-core calibration was achieved. The best porosity estimation is given by density neutron because it evidences clay content and calcareous cement correctly.

Subsidence Modeling Validation Through Back Analysis for an Italian Gas Storage Field

The multi-disciplinary work described in the paper was aimed at analyzing and predicting the cyclical ground surface movements induced by underground gas storage (UGS) activities in a depleted gas field located in the Po Plain (Italy). The field has been operated as a storage facility for nearly three decades. Currently, the possibility of delta-pressuring the reservoir (i.e. to increase the maximum operating pressure above the initial reservoir pressure) to enhance the storage performance is being considered. Significant information was collected over time: 2/3D seismic surveys, geological and sedimentological studies, 60+ logged wells, geotechnical lab tests and 50+ years of production history and monitoring were available for the development of a fully integrated static–dynamic–geomechanical analysis. The mechanical aspects of the study are the focus of this paper. The data coming from different sources at different scales were analysed and integrated to set up and characterize a 3D finite element method mechanical model to calculate the surface movements induced by UGS activity by adopting an elasto-plastic constitutive law. The model was then calibrated via a back analysis approach, i.e. the model parameters were fine-tuned so that the simulated subsidence/uplift would compare satisfactorily with the ground movements collected over nearly 10 years of monitoring via interferometric synthetic aperture radar analysis in the region under investigation. Eventually, the calibrated model was used as a forecasting tool for subsidence evaluation under different future storage strategies, including delta-pressuring conditions. Results proved that no significant subsidence is expected even if the maximum operating pressure reached 120 % of the initial formation pressure.

Improvements in understanding short-range permeability variability in coal seam gas reservoirs

After several years of production history on at least some of the more than 7,000 CSG production wells in the Surat and Bowen basins, reservoir engineers continue to note that understanding detailed permeability spatial variation near the well bore and its impact on actual production performance remains poor. There is a growing realisation that permeability of coals has an even higher variability than was initially expected, and that this variability occurs across a shorter range than that of the typical inter-well spacing (~750 m). As a result, flow between wells, pressure depletion, water and gas production rates and ultimate recovery is difficult to predict. Forecasting short-range continuity of different categories of absolute permeabilities through modelling is the key challenge. Other physical or geophysical parameters may change similarly with the same range. Generation models tend to over-estimate the lateral continuity of coals and associated carbonaceous shales resulting in a poor match between the model predictions and the observed production data. This may be due to incomplete information on the short-range variability of porosity and permeability and the appropriate up-scaled values for these parameters used in the reservoir simulation models. This extended abstract discusses controls on permeability, both the geological influences and the impact of drilling and completion on permeability. Taking a holistic approach to the problem of understanding permeability variability, the relative impact of these controls is estimated and discussed. With the benefit of rudimentary ranking of these controls, techniques have been developed to improve measurement and modelling of permeability variability. These approaches can help improve the predictive modelling capability of reservoir performance.

Reservoir Characterization and History Matching of the Horn River Shale: An Integrated Geoscience and Reservoir-Simulation Approach

Summary We present a systematic approach to integrate geoscience and dynamic reservoir modeling for two multiwell pads in the Horn River basin, Canada. The Horn River shale-gas play is a world-class unconventional gas resource and is being exploited by use of multistage hydraulic fracturing along horizontal wells. The two well pads, Pad-1 and Pad-2, selected for this study comprise eight and seven horizontal wells, respectively, with 1 to 7 years of production history. Numerical modelling of shale reservoirs has historically been a problematic low-confidence exercise because of the difficulties associated with inadequate characterization of the geologic framework of shale plays; the problems of estimating the properties of induced-fracture networks; and the complexities of capturing multiphase flow in fracture networks and wellbores during production, especially in the face of offset-well activity. This paper provides insights into these issues. The geoscience modelling activity begins with integrating information from cores, well logs, petrophysical analyses, and seismic data into a 3D geocellular model. At first, the model is built upon a simple lithostratigraphic concept, which is the basis of the numerical-flow-modelling exercise of Pad-1. The 3D geocellular model is thereafter thoroughly reworked to incorporate a sequence-stratigraphic perspective to the Horn River shale. This reworked geocellular model has a profound impact on the dynamic modelling of Pad-2. Also, hydraulic conductivity of induced and natural fractures is measured on Horn River core plugs at reservoir conditions to constrain conductivity values assigned to primary, secondary, and tertiary flow paths into dynamic reservoir modelling. As a result of the integrated work flow, we have achieved a history match allowing us to further understand the hydraulic-fracturing behaviour and its impact on producing shale reservoirs of the Horn River formation. On the basis of the findings, we recommend targeting the Evie and Otter Park shale reservoirs for landing horizontal wells and multistage fracturing when the Carbonate Fan is thin; this approach can produce all three compartmentalized shale reservoirs of the Horn River formation. Ultimately, the objective of any reservoir modelling project is to provide a range of reliable forecast of future performance that is grounded in representative geoscience interpretations and that takes operational constraints into account. The technical learnings described in this work will be helpful to further understand hydraulic-fracturing behaviour and its impact on producing shale reservoirs.

A New Assisted History Matching Approach for Complex Reservoirs

This article describes the integration of mathematical models (Simplex and steepest descent algorithm) with global modification (through deeply geological studies) to increase the efficiency of history matching. This method can take full advantage of the gradient-based method (fast rate of convergence) and guarantee of finding a global minimum for inverse problems. The above method is applied to the Lower Miocene reservoir at White Tiger field, a highly heterogeneous reservoir, which has been producing under a naturally edge-water mechanism. A model that covers the upper formation of White Tiger field with 27 production wells and 8 injection wells is conditioned to 23 years of production history. The new assisted history matching approach provides an excellent match between simulation results and production data.

Rapid engineering solutions for optimum reservoir management strategies in complex reservoirs: A field case study in Southern Mexico

The objective of this paper is to illustrate the value of rapid engineering analysis as a first step to delineate optimum reservoir management strategies in complex oil fields. Probabilistic material balance, fractional flow calculations, decline curve analysis, and cross-sectional reservoir simulation models are examples of fundamental engineering tools that can be used to quickly optimize a reservoir management strategy in a fraction of the time required for a full-cycle reservoir simulation study. This rapid approach is ideally suited to support fast-pace operational decision making. The proposed methodology is demonstrated with a complex naturally fractured field in Southern Mexico with more than 40 years of production history. The work presented here was used to redefine the short, medium and long-term reservoir management strategies of the field. The details of a recommended new generation water injection programme are also provided.

A Multiscale Workflow for History Matching in Structured and Unstructured Grid Geometries

SummaryWe present the development and field application of a workflow for multiscale reservoir-model calibration that seamlessly integrates production data into the reservoir description from the facies to the grid-cell scale. To start with, the permeability field is parameterized using a novel grid-connectivity-based transformation basis that can be applied with any model geometry, including unstructured and corner-point grids. The parameterization basis functions emerge from spectral decomposition of the grid-connectivity Laplacian and are related to the structural harmonics of the grid. To reconcile data with model resolution during history matching, we first use the coarsest-scale basis functions to identify the large-scale variability. Additional smaller-scale basis elements are then adaptively incorporated to successively refine the model to a level supported by data resolution. During refinement, the inclusion of more detailed basis functions into the parameterization is determined by generic modal frequency when the prior model is unavailable or by using prior information when available. In the final step of the workflow, a streamline-based inversion is performed to locally adjust the reservoir model at grid-cell resolution along preferential-flow paths defined during the coarser-scale parameterization.We demonstrate the suitability and effectiveness of the developed workflow through application to an offshore turbidite reservoir with frequent well intervention, including shut-ins and recompletions. The static model has over 300,000 cells, a complex channelized interpretation with faults, four injector/producer pairs with deviated wells, and over eight years of production history, including water cut and pressure data. The grid-connectivity-based parameterization effectively updates the prior regional permeability at scales and in locations warranted by the data, while preserving the geologic continuity and avoiding ad hoc redefinition of regions given the sparse well pattern. The multiscale calibrated-permeability field indicates flow communication previously unrecognized in static geologic interpretation or manual history matching.

Automatic History Matching Using the Integration of Response Surface Modeling with a Genetic Algorithm

History matching is the process of updating a petroleum reservoir model using its production history. It is a required step before a model is accepted for forecasting production. This work aims at integrating response surface modeling with a genetic algorithm to realize a global optimization method for the history matching process. To achieve this, a proxy model was constructed for the flow simulator outputs for all measurements that enter a global objective function. A genetic algorithm was applied to minimize the proxy model. Then a reservoir model was built based on this minimized proxy model. Construction of a good proxy model requires only a limited number of simulation runs and its simulation time is a couple of seconds. Therefore, the evolution of proxy model using a genetic algorithm takes a short time. Compared to other global optimization techniques, this method can perform the history matching process in a quick, low-cost manner. The method was validated by a field case study. The simulation model used contains 41 years of production history. During the history matching process, a limited number of simulation runs (79) was used to construct a high-quality proxy model and by application of genetic algorithm, the global objective function was reduced from 581.362 to 9.347. The observed oil rate, shut-in pressure, repeated formation test pressure, and water cut (must be zero) were matched using the proposed method.