Estimated Ultimate Recovery Research Articles

Automatic History Matching of Parent-Child Wells in Shale Gas Reservoirs with Complex Fracture Hits

Abstract It is excessively crucial for performing production match of parent-child wells automatically by using assisted history matching approach, especially considering the interwell interference scenario. We explored a new EDFM-AI (Embedded Discrete Facture Model-Artificial Intelligence) workflow by taking into account the multiple uncertainty parameters that were composed of fracture height, fracture half-length, fracture conductivity, fracture water saturation, fracture cluster efficiency, and natural fracture conductivity. The history matching algorithm implemented in this study is advanced machine learning model called XGBoost. It could overcome the issue that the proxy model is always imprecise due to the limited training sample data. Specifically, we established a field-scale reservoir model that includes parent-child shale gas wells with fracture hits in the Sichuan basin. By using this sophisticated workflow, two wells’ history matching results are excellent in terms of the inversed solution of the hydraulic fracture properties and natural fracture properties. And then, we perform the production forecasting for parent and child wells, respectively. Consequently, the results show that the best match value for estimated ultimate recovery (EUR) of parent well is 287.4 million cubic meters whereas the best match value for EUR of child well is 187.6 million cubic meters. The reliable reason is that effective fractured area of parent well is larger than that of child well. Besides, the natural fractures have a significant impact on the performance of shale gas wells with fracture hits observed, especially in the short-term production period.

Machine learning based rate decline prediction in unconventional reservoirs

The main objective of this paper is to develop a novel machine learning based model that can accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells. This is because decline curves are easier and faster alternative to complex reservoir simulators which perform computationally expensive operations. In contrast to this, decline curves require only a few parameters in the equation which can be easily collected from the existing data of the wells. In this study, first we collected the well data corresponding to well parameters such as initial monthly oil flow rate ( q i ), well completion parameters (i.e., no. of fracturing stages, completed length, amount of proppant used, amount of fracturing fluid used), well location parameters (TVD Heel-Toe Difference), reservoir fluid properties (Oil API Gravity, initial 24 h period Gas-Oil Ratio (GOR), initial 24 h period Gas Produced, initial 24 h period Oil Produced), flowing tubing pressure, casing pressure tubing size,choke size from publicly available databases of the Eagle Ford Shale formation Texas RRC (Railroad Commission of Texas). Wells were selected randomly and only those wells were finally included for the study whose data of the all the required parameters were available. The model parameters were estimated by fitting the production data to the decline curve models. Artificial Neural Network (ANN) was employed to build Machine learning models as a function of the above well parameters for the corresponding model parameters. The decline curves for new or existing wells were rapidly predicted using these models. In order to estimate the predictive accuracy of these models when applied to new or test wells cross validation technique such as k-fold cross validation was employed. These models were also used to predict EUR for the test wells. Additionally, feature selection was also done using algorithms such as Chi Square Test (χ2) and Minimum Redundancy Maximum Relevance (MRMR) Algorithm to determine the relative importance of predictor variables in predicting EUR. The predictor variables were successfully linked to SEDM (Stretched Exponential Decline Model) decline curve parameters (n and τ) in a random set of oil field well data. The relative influences of various well parameters were also examined to determine the hidden relationship between them. The novelty in this study lies in the algorithm and dataset that we used for the rate decline prediction in Eagle Ford data set. Although, this paper has referenced some previous papers where machine learning has been used to make prediction, but this paper presents use of new algorithm as well as a new dataset. As more data gets available, there is definitely extra room for further data analysis and improved results using machine learning methods. In future, the number of wells can be increased and the updated results can again be submitted after investing more time. It should be noted here that data downloading and preparing takes most of the time for such study especially when dealing with oil and gas data.

Generalized Extreme Value Statistics, Physical Scaling and Forecasts of Oil Production from All Vertical Wells in the Permian Basin

We analyze nearly half a million vertical wells completed since the 1930s in the most prolific petroleum province in the U.S., the Permian Basin. We apply a physics-guided, data-driven forecasting approach to estimate the remaining hydrocarbons in these historical wells and the probabilities of well survival. First, we cluster the production data set into 192 spatiotemporal well cohorts based on 4 reservoir ages, 6 sub-plays, and 8 completion date intervals. Second, for each cohort, we apply the Generalized Extreme Value (GEV) statistics to each year of oil production from every well in this cohort, obtaining historical well prototypes. Third, we derive a novel physical scaling that extends these well prototypes for several more decades. Fourth, we calculate the probabilities of well survival and observe that a vertical well in the Permian can operate for 10–100 years, depending on the sub-play and reservoir to which this well belongs. Fifth, we estimate the total field production of all existing vertical wells in the Permian by replacing historical production from each well with its prototype. We then time-shift and sum up these prototypes together, obtaining 34 billion barrels of oil as estimated ultimate recovery (EUR). Our most notable finding is that the rate of finding big reservoirs in the Permian has been declining drastically and irreversibly since the 1970s. Today, operators need to drill wells that are twice as deep as the 1930s’ wells, yet they produce 4–12 times less.

Reservoir characteristic and its relationship with estimated ultimate recovery of Jurrasic tight shelly limestone reservoir in the central Sichuan Basin, Southwest China

A combination of reservoir geological factors and a decline curve approach were applied in a play wide analysis, to reveal the relationship between the reservoir characteristics and estimated ultimate recovery (EUR) of the tight shelly limestone reservoir of Da'anzhai formation. As a result, all the oil bearing matrix pores and fractures in the reservoir correspond to the exponential decline, logarithmic decline and power law exponential decline with diminishing EURs of 86.1 MSTB, 39.3 MSTB and 5.9 MSTB (Mille Standard Tank Barrel) (P50) respectively. The proportions of megapores, macropores, mesopores, micropores, and nanopores in the reservoir were 1.6%, 3.7%, 18.1%, 27.8% 27.8%, and 48.8% respectively, and the corresponding EUR ranges tended to be above 294 MSTB, 110 MSTB to 294 MSTB, 37 MSTB to 110 MSTB, 4 MSTB to 37 MSTB, and below 4 MSTB. Thicknesses of fracture segments and thickness ratio's of dissolved cave segments to the reservoir were positively linearly related to the EUR. [Received: April 13, 2021; Accepted: November 26, 2021]

Machine learning-based estimated ultimate recovery prediction and sweet spot evaluation of shale oil

In this paper, we propose a machine learning-based, multi-discipline integrated evaluation workflow to evaluate sweet spots. We predict the estimated ultimate recovery (EUR) map and evaluate sweet spots at different oil price scenarios in the study area. The results show that the correlation coefficient between well EUR and predicted EUR is 0.9247. At the oil price of $40, $50, and $60/bbl, the sweet spot areas are 3.31 km2, 27.75 km2, and 51.61 km2, and the total economically recoverable reserves are estimated to be 2.46 × 105 t, 14.02 × 105 t, 26.91 × 105 t respectively. It is concluded that machine learning model is an excellent way to auto learn the relationship between complex shale reservoir variables with EUR. It is innovative to show the distribution of sweet spots according to oil price. This workflow can be wildly used in the shale oil exploration and development to evaluate investments and optimise well placement. [Received: August 26, 2020; Accepted: April 19, 2021]

The paradox of increasing initial oil production but faster decline rates in fracking the Bakken Shale: Implications for long term productivity of tight oil plays

In the US, tight oil is the largest source of liquid hydrocarbons and has enabled the country to become the world's largest oil producer. The estimated ultimate recovery (EUR) and rate of production decline are key metrics in the evaluation of the future productivity of tight oil wells. We chose the Bakken Shale because of its high quality, publicly available data. Traditionally, well operators have estimated the EUR for each well from the initial production (IP), using empirical type curves to extrapolate to the ultimate production. From 2015 to 2018 the IP of the average Bakken well increased by approximately 50%. This increase resulted in claims by operators and in the academic literature that more intense hydraulic fracturing was increasing the average EUR of the Bakken play. At the same time, other observers claimed the wells declined much more rapidly than previous wells. This faster decline provided evidence for lower ultimate production from newer wells. The aim of this study was to understand the origin of these seemingly conflicting observations. A physics-based, scaling model was used to predict production from horizontal multi-stage fractured wells. The model for oil recovery is based on pressure diffusion through fractured porous media. The model assumes that the rock is incompressible, the permeability and oil saturation are constant, the water is incompressible, and the viscosity is slowly varying with space. The scaling model was applied to 13,444 wells. The EUR and terminal decline rate (TDR) were estimated from fitting production to our scaling model. Our study found that implementation of more intensive hydraulic fracturing resulted in higher IP and steeper terminal-production declines. Recently published results estimating the total production from the Bakken that include increased lifetime production commensurate with observed increases in average IP, significantly overestimate the long-term production potential of tight oil, both in the US and globally.

An improved empirical model for rapid and accurate production prediction of shale gas wells

The estimated ultimate recovery (EUR) of a single shale gas well is one of the important evaluation indicators for the scale and benefit development of shale gas. Nevertheless, due to the lack of engineering and geologic data, as well as the limitations of the existing empirical methods, the current production forecasting methods cannot make reasonable predictions for EUR. Therefore, Duong method is used as the prototype in this paper, a rapid and accurate empirical model for improved production prediction is proposed based on the historical production profile analysis of mass producing wells in Sichuan Basin. The accuracy and universality of the new model are verified by production examples in the Weiyuan, Changning, Luzhou and Yuxi blocks of the Sichuan Basin. The examples illustrate that the new model can be applied well in typical wells, and has no limitation of flow regimes for the production prediction of gas wells. In order to solve the problem of production data fluctuation and decline curve deviation caused by stimulation measures in the production process, a multi-segment production prediction model analysis method is proposed based on the improved model in this study and has been well applied in the field. Most importantly, the new model has clear physical parameters and is easy to operate. Performance of the new model based on production data is as reasonable as the modern production decline methods (including analytical model), which is better than existing empirical methods. • Proposing an improved empirical model, which is rapid and accurate. • Less parameters were selected, the new model still has clear physical meaning. • Application condition of the new model is not limited by the flow regime. • Performance of the new model based on production data is as reasonable as the analytical model. • Multi-segment production prediction model is well applied to gas wells with production data fluctuation.

A real-world impact of offset frac-hits by rate transient analysis in the Bakken and Three Forks, North Dakota, USA

Production via multi-fractured horizontal wells with specific spacing units is a common development plan in shale plays. Commonly, operators drill and complete a single well in order to hold a 1280-acre spacing unit and once the acreage is secured across the asset, infill wells at each spacing unit are drilled so, depletion from the original (parent) well can be observed within the spacing unit. This will increase the likelihood of existing wells to experience inter-communication when infill wells are hydraulically fractured which has detrimental effects. Hence, understanding the effects of well timing and spacing on the performance of the overall spacing unit is critical to choose an appropriate development plan. In this regard, rate transient analysis (RTA) is an effective way to quantify the impact of offset frac-hits, hinting the changes in reservoir properties such as stimulated reservoir volume (SRV) and well productivity. In this real-world study, we used pseudo normalized pressure versus material balance square root of time plots to determine the impact of offset frac-hits on existing wells in Williston Basin, ND. The slope of the superposition time plot is inversely proportional to the product of the contacted surface area (Ac) and the square root of reservoir permeability (k), Ack, which was a good metric to evaluate early time productivity and completion effectiveness. Additionally, production look-back performed on 71 operating wells using decline curve analysis (DCA) confirmed changes in reservoir properties and production performance to determine appropriate well spacing and infill timing. Next, economic analysis for 71 wells and 13 spacing units, using a reasonable/favorable commodity pricing, to assess the investment efficiencies of each project was carried out. Results showed that offset frac-hits cause up to 50% estimated ultimate recovery (EUR) loss, up to 80% water-cut increment, and more than 40%, Ack reduction for parent wells. Wells with multi-sleeve single ball (MSSB) completion system would have 32% lower cumulative oil production per lateral foot and 50% lower EUR than open hole sliding sleeve and cemented liner plug and perf (P-n-P). Finally, more proppant and fracturing fluid volume loading not only will not increase the production, but also deteriorates well performance. Collectively, this study indicated that 5–6 wells per 1280-acre spacing is the optimal well spacing unit within the study area to set a guideline for successful future operations. Ultimately, the very detailed field practical aspects presented here which is limited in the literature and combined with theories of RTA, can become a benchmark for further verification of mathematical models.

Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models

Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the recurrent neural network (RNN), long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) neural network, and their performance in the tight gas reservoir production prediction is investigated and compared. To further improve the performance of the sequence learning method, the hyperparameters in the sequence learning methods are optimized through a particle swarm optimization algorithm, which can greatly simplify the optimization process of the neural network model in an automated manner. Results show that the optimized GRU and RNN models have more compact neural network structures than the LSTM model and that the GRU is more efficiently trained. The predictive performance of LSTM and GRU is similar, and both are better than the RNN and the decline curve analysis model and thus can be used to predict tight gas production.

Workflow for Probabilistic Resource Estimation: Jafurah Basin Case Study (Saudi Arabia)

This study provides a workflow and preliminary estimations of the estimated ultimate recovery (EUR) volumes for natural gas and condensate liquids in the Tuwaiq Mountain Formation, the principal target in the Jafurah Field development project in Saudi Arabia. The strategic need for the field development is reviewed and the field characteristics are outlined based on public data sources complemented with data from analogous reservoirs. The target zone in the Jafurah Basin is a carbonaceous shale, being developed with up to 10,000-ft-long multistage-fractured laterals with 30 ft perforation cluster spacing and an assumed typical 1250 ft well spacing. The field will come on stream in 2024, when the gas-gathering pipeline system, natural gas processing plant, and underground gas storage facilities will all be in place. The range of uncertainties in the key reservoir parameters is taken into account to estimate preliminary EUR volumes (P90, P50, and P10) for both gas and condensates. Based on the present and prior EUR estimations, it can be concluded that the Jafurah Basin comprises one of the largest unconventional field development projects outside of North America.

A Two-Phase Flow Model for Reserves Estimation in Tight-Oil and Gas-Condensate Reservoirs Using Scaling Principles

Summary A common approach to forecast production from unconventional reservoirs is to extrapolate single-phase flow solutions. This approach ignores the effects of multiphase flow, which exist once the reservoir pressure falls below the bubble/dewpoint. This work introduces a new two-phase (oil and gas) flow solution suitable to extrapolating oil and gas production using scaling principles. In addition, this study compares the application of the two-phase and the single-phase solutions to estimates of production from tight-oil wells in the Wolfcamp Formation of west Texas. First, we combine the oil and the gas flow equations into a single two-phase flow equation. Second, we introduce a two-phase pseudopressure to help linearize the pressure diffusivity equation. Third, we cast the two-phase diffusion equation into a dimensionless form using inspectional analysis. The output of the model is a predicted dimensionless flow rate that can be easily scaled using two parameters: a hydrocarbon pore volume and a characteristic time. This study validates the solution against results of a commercial simulator. We also compare the results of both the two-phase and the single-phase solutions to forecast wells. The results of this research are the following: First, we show that single-phase flow solutions will consistently underestimate the oil ultimate recovery factors (URFs) for solution gas drives. The degree of underestimation will depend on the reservoir and flowing conditions as well as the fluid properties. Second, this work presents a sensitivity analysis of the pressure/volume/temperature (PVT) properties, which shows that lighter oils (more volatile) will yield larger recovery factors for the same drawdown conditions. Third, we compare the estimated ultimate recovery (EUR) predictions for two-phase and single-phase solutions under boundary-dominated flow (BDF) conditions. The results show that single-phase flow solutions will underestimate the ultimate cumulative oil production of wells because they do not account for liberation of dissolved gas and its subsequent expansion (pressure support) as the reservoir pressure falls below the bubblepoint. Finally, the application of the two-phase model provides a better fit when compared with the single-phase solution. The present model requires very little computation time to forecast production because it only uses two fitting parameters. It provides more realistic estimates of URFs and EURs, when compared with single-phase flow solutions, because it considers the expansion of the oil and gas phases for saturated flow. Finally, the solution is flexible and can be applied to forecast both tight-oil and gas condensate wells.

Data-driven model-based rate decline prediction in unconventional eagle ford shale oil wells

The main objective of this paper is to develop a novel data-driven-based model that can accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells. This is because decline curves are easier and faster alternative to complex reservoir simulators which perform computationally expensive operations. In contrast to this, decline curves require only a few parameters in the equation which can be easily collected from the existing data of the wells. The predictor variables were successfully linked to SEDM (Stretched Exponential Decline Model) decline curve parameters (n and τ) in a random set of oil field well data. The relative influences of various well parameters were also examined to determine the hidden relationship between them. The novelty in this study lies in the algorithm and dataset that we used for the rate decline prediction in Eagle Ford data set. Although, this paper has referenced some previous papers where machine learning has been used to make prediction, but this paper presents use of new algorithm as well as a new dataset. As more data get available, there is definitely extra room for further data analysis and improved results.

The Main Controlling Factors and Evaluation Method of the Reservoir Stimulation Potential: A Case Study of the Changning Shale Gas Field, Southern Sichuan Basin, SW China

Based on logging, seismic, fracturing and production data from 301 productive wells in Longmaxi Formation in Changning shale gas field of southern Sichuan basin, the various influence factors of shale gas stimulated potential have been analyzed to carry out the correlation study with estimated ultimate recovery (EUR) of the same fracturing operation intensity to fully clarify the main controlling factors for shale gas stimulated potential. The results show that matrix brittleness, fracture propagation and reservoir properties are the 3 key secondary potentials that control shale gas stimulation potential. The matrix brittleness is controlled by elastic modulus, Poisson’s ratio, Type I and II fracture toughness, which reflects the uniformity of hydraulic fracture propagation. Fracture propagation is controlled by critical net pressure, which reflects the scale of hydraulic fracture propagation. Reservoir properties are controlled by porosity, total organic carbon content (TOC) and horizontal interval differences, which reflect the enrichment conditions and dynamic production capacity of reservoir resources. The matrix brittleness index, fracture propagation index, reservoir properties index and their combined stimulated potential index were formed by using the above 7 parameters to verify and apply the wells in Changning shale gas field. Results show that the matrix brittleness index and length-width ratio of hydraulic fracture was significantly negative correlation, the fracture propagation index and stimulated reservoir volume (SRV) were significantly positive correlation, stimulated potential index was developed taking into account enrichment & exploitation potential. In areas where the value is greater than 0.5, increasing the fracturing scale can effectively improve the well productivity; in areas less than 0.5, increasing the fracturing scale has an upper limit on the increase of productivity, so the physical properties of the reservoir itself play a significant role in controlling the production of shale gas wells.

Hybrid physics and data-driven modeling for unconventional field development and its application to US onshore basin

The objective of this study is to develop a hybrid model by combining physics and data-driven approach for optimum unconventional field development. We used physics-based reservoir simulations to generate training datasets. These uncalibrated priors were incorporated into data-driven machine learning (ML) algorithms so that the algorithms can learn the underlying physics between reservoir simulation input and output, cumulative oil production for parent and child wells. The reservoir simulations in a single half-cluster model consists of injection and production phases. This allows the algorithm to relate the impact of completion design, well spacing and timing to the production profiles. After performing a sensitivity analysis to reduce the number of training inputs, more than 20,000 simulations results were generated as the training data. The best accuracy, R2 = 0.94, was achieved with the neural network model after tuning hyper-parameters. Then, we incorporated the hybrid model with the genetic algorithm to perform efficient history matching to calibrate model parameters and predict estimated ultimate recovery (EUR) and net present value with 10% discount rate (NPV10). The great efficiency in the hybrid model takes only several minutes to complete a single well history matching. The calibrated model shows that the prediction from the hybrid model is physically meaningful compared to actual reservoir simulations, capturing the impact of fracture geometry, child well spacing and timing on the well production. With multiple history matching results, we populated value acreage maps (e.g., EUR and NPV10) which can benefit the unconventional field development planning. Lastly, a blind test was conducted in an area of interest to demonstrate that the proposed workflow can effectively predict a well performance in the area without sufficient data, utilizing calibrated model parameters from history matched results of adjacent wells. The proposed model can provide quick and scalable solutions that honor underlying physics to help decision making on unconventional field development based on production profile and economic metrics with various completion designs.

A deep-learning-based prediction method of the estimated ultimate recovery (EUR) of shale gas wells

The estimated ultimate recovery (EUR) of shale gas wells is influenced by many factors, and the accurate prediction still faces certain challenges. As an artificial intelligence algorithm, deep learning yields notable advantages in nonlinear regression. Therefore, it is feasible to predict the EUR of shale gas wells based on a deep-learning algorithm. In this paper, according to geological evaluation data, hydraulic fracturing data, production data and EUR evaluation results of 282 wells in the WY shale gas field, a deep-learning-based algorithm for EUR evaluation of shale gas wells was designed and realized. First, the existing EUR evaluation methods of shale gas wells and the deep feedforward neural network algorithm was systematically analyzed. Second, the technical process of a deep-learning-based algorithm for EUR prediction of shale gas wells was designed. Finally, by means of real data obtained from the WY shale gas field, several different cases were applied to testify the validity and accuracy of the proposed approach. The results show that the EUR prediction with high accuracy. In addition, the results are affected by the variety and number of input parameters, the network structure and hyperparameters. The proposed approach can be extended to other shale fields using the similar technic process.

Development Evaluation and Optimization of Deep Shale Gas Reservoir with Horizontal Wells Based on Production Data

The implementation of horizontal wells is a key to economic development of the deep shale gas reservoir. In order to optimize the key parameters for drilling, stimulation, and the production system, the development effect of a horizontal well in deep shale gas formations was investigated from various aspects in this study. The drilling, fracturing, and production performances of this well were analyzed combining with the geological characteristics. The main technical problems and key factors that restrict the gas well performance and estimated ultimate recovery (EUR) were clarified. Through the integrated study of geology and engineering, the optimization strategies for increasing gas production and EUR are provided. The Z2 area, where the Z2-H1 well is located, has good reservoir physical properties, which bring a high drilling efficiency. However, there are still some problems during its development, such as poor fracture extension both horizontally and vertically, limited stimulated reservoir volume (SRV), rapid production declining, large water production, and serious liquid accumulation. In this study, a comprehensive approach was proposed that can improve single-well production and EUR by optimizing the target position, horizontal section length, pathway, spacing, new drilling and fracturing technology, and production system.

Techno-economic and sensitivity analysis of shale gas development based on life cycle assessment

For shale gas resevoirs development, many researches focus on how to improve the estimated ultimate recovery (EUR), and ignore the relation of economic benefits and EUR. Therefore, based on the research of EUR evaluation procedure as the basis of economic estimation, the relation of economic benefits and EUR is revealed in detail, and the economy-geology-engineering (EGE) model is creatively built. The results show that six researched wells all have economic benefits: the average EUR is 1.60 × 108 m3, and the after-tax averages of net present value, financial internal rate of return and investment payback period (Pt) are 434.32 × 104 USD, 10.39% and 4.45 years respectively. Combining with the seepage theory and on-site experience, the negative relation of Pt and development degree of free gas is determined. The economic benefits for the unit volume of shale gas are close about 0.03 USD/m3 under the current development technologies. The influence degrees of economic parameters from large to small are gas price, drilling cost, fracturing cost, business cost and subsidy respectively. The proposed EGE model can effectively predict the economic benefits with the complex geology-engineering factors in the Changning block, and the corresponding method can be generalized to more fields.

Cyclic Gas Injection EOR in Eagle Ford Can Increase Estimated Ultimate Recovery

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200427, “Evaluation of Eagle Ford Cyclic Gas Injection EOR: Field Results and Economics,” by George Grinestaff, SPE, Chris Barden, and Jeff Miller, SPE, Shale IOR, prepared for the 2020 SPE Improved Oil Recovery Conference, originally scheduled to be held in Tulsa, 18–22 April. The paper has not been peer reviewed. Cyclic-gas-injection-based enhanced oil recovery (CGEOR) in the Eagle Ford was begun in late 2012 by EOG Resources and, at the time of writing, has expanded to more than 30 leases by six operators (266 wells). An extensive EOR evaluation was initiated to analyze the results recorded in these leases. The authors write that CGEOR in Eagle Ford volatile oil can yield substantial increases in estimated ultimate recovery (EUR) with robust economics, depending on compressor use and field life. Introduction Eagle Ford Source Rock and Reservoir. The Eagle Ford shale represents some of the world’s richest source rocks. The Upper Cretaceous seafloor received abundant organic debris and preserved it in an anoxic environment. The low permeability of the shale and limestone helped generate hydrocarbons when pore pressure exceeded overburden pressure. The resulting natural fractures provided a means to expel oil, much of it migrating into the overlying Austin Chalk and Tertiary sandstones. The primary target area for produced-gas injection EOR is currently in the volatile oil window between 9,000 and 11,000 ft true vertical depth, which yields oil API gravity of greater than 40. Initial gas/oil ratio (GOR) typically ranges from 1,000 to 3,000 scf/bbl. Eagle Ford EOR History. The first large-scale CGEOR project was implemented in October 2014. Rapid development has occurred since then, but, in the complete paper, the authors present the first commercial EOR projects by EOG Resources because these have the longest CGEOR production history. Recent projects show more-efficient startup, cycling, and higher optimization of gas injection. Therefore, the analysis of EOR in this paper takes a conservative approach of using the first projects because they appear to have lower EOR recovery but more production history. Evaluation Methodology Unconventional EOR Work Flow. Analysis of CGEOR production and results has been completed using production history and reservoir simulation to provide a rigorous evaluation. The authors use a 14-component fracture element model with a very fine grid to predict well GOR, EUR, and reservoir behavior for the compositional process. The element model is then scaled up to mimic the average well for a given pad or lease, and then cycle operations are developed based on CGEOR simulation runs and criteria. Unconventional CGEOR provides a direct response after the first cycle of gas injection; however, the base depletion profile also is important for understanding economics for increased oil production or incremental EOR. A history match of the base depletion is first completed to match an average well at the pad level (approximately one 640-acre section with 10 to 14 wells). The element is then scaled up based on well completion, stimulated rock volume, and EUR for the base depletion.

Getting Your Perforations in a Line Can Mean More-Productive Fracturing

Want more production from a shale well? Consider lining up the perforations. A handful of speakers at the recent SPE Hydraulic Fracturing Technology Conference talked about improved fracturing results with oriented perforating—shooting the holes at the same place in the casing, often the top. This breaks from designs that arranged the holes in a helical pattern with each charge angled 60° from the previous one. “We did see indications we are getting better production from oriented perforating,” said Blake Horton, senior completions engineer for Ovintiv (SPE 204177). Production gains were also reported by ConocoPhillips which compared production from similar wells with and without oriented perforating. The analysis was designed to filter out differences in the geology, drilling, and completions. It concluded the value of the added production far exceeded the $20,000-per-well cost of installing the assembly, including a weight bar to tilt the perforating guns into position. “That’s less than the undiscounted value of 400 barrels of oil. An internal study indicated that ConocoPhillips improved estimated ultimate recovery (EUR) by a minimum of 5% when using high-side-oriented perforating,” said Dave Cramer, senior engineering fellow at ConocoPhillips and an early advocate for the method. “For an initial choked flow rate of 1,000 B/D, the payout on investment is 10 days or less,” he said. Ovintiv declined to provide a number, but Horton said ConocoPhillips’ estimate is within Ovintiv’s range based on similar comparisons of wells with and without oriented perforating. That number is at the low end of the estimates offered in discussions about oriented perforating performance at the conference. Higher estimates are questioned by those who doubt the test results can be sustained when the method is scaled up. What was certain is the number of users is rising and includes names such as Shell and Chevron. “We found that oriented perforating definitely helps to treat all the clusters,” said Jon Snyder, a staff completion engineer for ConocoPhillips who presented the paper, adding, “by oriented perforating we mean that when we are perforating, we aim for the high side of the wellbore” (SPE 204203). When Horton polled the audience at a recent talk, more than half of the respondents said they were using gun systems designed to orient the perforating charges at a target angle. “A year from now, few people will not be doing oriented perforating; the advantages of it are clear,” Cramer said. He has been promoting the idea within the company for years with mixed acceptance.

Key geological factors controlling the estimated ultimate recovery of shale oil and gas: A case study of the Eagle Ford shale, Gulf Coast Basin, USA

Based on 991 groups of analysis data of shale samples from the Lower Member of the Cretaceous Eagle Ford Formation of 1317 production wells and 72 systematic coring wells in the U.S. Gulf Basin, the estimated ultimate recovery (EUR) of shale oil and gas of the wells are predicted by using two classical EUR estimation models, and the average values predicted excluding the effect of engineering factors are taken as the final EUR. Key geological factors controlling EUR of shale oil and gas are fully investigated. The reservoir capacity, resources, flow capacity and fracability are the four key geological parameters controlling EUR. The storage capacity of shale oil and gas is directly controlled by total porosity and hydrocarbon-bearing porosity, and indirectly controlled by total organic carbon (TOC) and vitrinite reflectance (R o ). The resources of shale oil and gas are controlled by hydrocarbon-bearing porosity and effective shale thickness etc. The flow capacity of shale oil and gas is controlled by effective permeability, crude oil density, gas-oil ratio, condensate oil-gas ratio, formation pressure gradient, and R o . The fracability of shale is directly controlled by brittleness index, and indirectly controlled by clay content in volume. EUR of shale oil and gas is controlled by six geological parameters: it is positively correlated with effective shale thickness, TOC and fracture porosity, negatively correlated with clay content in volume, and increases firstly and then decreases with the rise of R o and formation pressure gradient. Under the present upper limit of horizontal well fracturing effective thickness of 65 m and the lower limit of EUR of 3×10 4 m 3 , when TOC<2.3%, or R o <0.85%, or clay content in volume larger than 25%, and fractures and micro-fractures aren’t developed, favorable areas of shale oil and gas hardly occur.