Estimated Ultimate Recovery Research Articles

Geological and Engineering Integration Fracturing Design and Optimization Study of Liushagang Formation in Weixinan Sag

The Weixinan Sag in the Beibuwan Basin is rich in shale oil resources. However, the reservoirs exhibit rapid phase changes, strong compartmentalization, thin individual layers, and high-frequency vertical variations in the thin interbedded sandstone and mudstone. These factors can restrict the height of hydraulic fracture propagation. Additionally, the low-porosity and low-permeability shale oil reservoirs face challenges such as low production rates and rapid decline. To address these issues, the Plannar3D full 3D fracturing model was used to simulate hydraulic fracture propagation and to study the main controlling factors for fracture propagation in the second member of the Liushagang Formation. Based on the concept of geological–engineering integration, a sweet spot evaluation was conducted to identify reservoirs with relatively better brittleness, reservoir properties, and oil content as the fracturing targets for horizontal wells. The UFM model was then applied to optimize fracturing parameters. This study indicates that the matrix-type oil shale has a high clay mineral content, resulting in a low Young’s modulus and poor brittleness. This makes hydraulic fracture propagation difficult and leads to less effective reservoir stimulation. In contrast, hydraulic fractures propagate more easily in high-brittleness interlayer-type oil shale. Therefore, it is recommended to prioritize the extraction of shale oil from interlayer-type oil shale reservoirs. The difference in interlayer stress is identified as the primary controlling factor for cross-layer fracture propagation in the study area. Based on the concept of geological–engineering integration, a sweet spot evaluation standard was established for the second member of the Liushagang Formation, considering both reservoir quality and engineering quality. Four sweet spot zones of interlayer-type oil shale reservoirs were identified according to this evaluation standard. To achieve uniform fracture initiation, a differentiated segment and cluster design was implemented for certain high-angle sections of well WZ11-6-5d. Interlayer-type oil shale was selected as the fracturing target, and the UFM was used for hydraulic fracture propagation simulation. Fracturing parameters were optimized with a focus on hydraulic fracture characteristics and the estimated ultimate recovery (EUR). The optimization results were as follows: a single-stage length of 50 m, cluster spacing of 15 m, pump injection rate of 10 m3/min, fluid intensity of 25 m3/m, and proppant intensity of 3.5 t/m. The application of these optimized fracturing parameters in field operations resulted in successful fracturing and the achievement of industrial oil flow.

Enhancing Shale Gas EUR Predictions with TPE optimized SMOGN: A Comparative Study of Machine Learning Algorithms in the Marcellus Shale with an Imbalanced Dataset

Oil and gas operators frequently rely on traditional methods to predict Estimated Ultimate Recovery (EUR) but, these methods often fail to accurately predict shale gas EUR. Therefore, machine learning (ML) algorithms were shown as promising alternatives but, the negative effects of imbalanced datasets on their performance still remains underexplored. This study addresses this gap with a Tree-Parzen Estimator (TPE) optimized Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) to alleviate the detrimental effects of the imbalanced datasets on model performance. Two cases were compared: one employed standard pre-processing while, the other employed TPE optimized SMOGN. Four ML algorithms: Artificial Neural Network (ANN), Deep-ANN, Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) were trained across both cases with an imbalanced dataset of 460 Marcellus shale wells. Exploratory data analysis revealed that the imbalance was due to the evolution of completion techniques, leading to the underrepresentation of wells completed with more recent, aggressive methods. The proposed framework improved of the models from 0.8243 - 0.8934 to 0.8851 - 0.9186 with more significant gains in the underrepresented wells in the higher EUR regions (), where the improved from 0.2155 - 0.4598 to 0.5615- 0.9472. The SMOGN enhanced SVR had the highest computational efficiency ( to train) and highest performance of 0.9472) in these higher EUR regions, while the SMOGN enhanced Deep-ANN had the highest overall performance ( of 0.9186). This framework outperforms standard pre-processing frameworks. Additionally, it enables operators to predict shale gas EUR more accurately for future infill wells completed with recent techniques, even while the wells are still producing in transient flow, facilitating early cost-saving decisions. To the best knowledge of the authors, this is the first research that proposed TPE optimized SMOGN to improve shale gas predictions.

Study on the deployment of differentiated well patterns for shale gas in southern Sichuan region

Shale gas blocks are of great geological differences in southern Sichuan, deploying differentiated well patterns accordingly is therefore significant. For two typical shale gas blocks in southern Sichuan, Dingshan normal pressure area and Yongchuan south area, a hydraulic fracturing interlayer law model based on the cohesive zone model and a fracture extension model based on rock damage theory were established. Hydraulic fracture height and length charts under different natural fracture density and operation parameters were drawn. Using these charts, fracture parameters in different areas were determined, and hydraulic and natural fracture models were established. By coupling an embedded discrete fracture model with geological models and fracture models, a complex fracture network simulation model was built to predict gas production. Horizontal well spacing, orientation, and length were optimized under different geological characteristics to maximize production and economic indicators, and a differentiated well pattern deployment strategy was proposed. Results show that, firstly, for Dingshan area, the fracture height range is 40-64 m with an average of 51 m, that of Yongchuan is 42-65 m with an average of 53 m. The fracture length range of Dingshan area is 215-350 m with an average of 300 m, whereas that of Yongchuan is 290-350 m with an average of 330 m. The optimal horizontal well spacing in Dingshan area is 300 m, the optimal well orientation is 60°-90°, and the optimal lateral length is 2000 m; and those of Yongchuan are 300 m, 90°, and 1680 m. Well pattern on-site can be adjusted according to the optimal parameters to achieve higher Estimated Ultimate Recovery (EUR) and Net Present Value (NPV).

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

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

Research on Mechanism and Effect of Enhancing Gas Recovery by CO2 Huff-n-Puff in Shale Gas Reservoir.

The technology of CO2-enhanced gas recovery (CO2-EGR) plays a pivotal role in the CCUS (Carbon Capture, Utilization, and Storage) industry, which helps to achieve a win-win situation of economic benefit and environmental benefit for gas fields. Shale gas reservoirs, with their unique geological and surface engineering advantages, are one of the most promising options for CCUS implementation. Focusing on shale formations within the mid-deep blocks of the Sichuan Basin, this study conducted competitive adsorption experiments using multicomponent gases. Through physical simulations and single-well numerical modeling, factors such as injection volume, timing, shut-in time, and huff-n-puff rounds were examined for their impact on recovery. The results show that the higher the CO2 content in the injected medium, the more pronounced advantage in gas adsorption on shale surfaces. Optimal performance was achieved with a CO2 content in the injection medium of 80% to 100%, an injection volume of 0.2-0.3 PV, a shut-in time exceeding 6 h, and a relatively delayed injection timing. The recovery in the first round of huff-n-puff was increased by 24.2% to 47.8%, which gave a full play to the role of huff-n-puff and achieved favorable benefits. Based on the middle-deep geological parameters, a single-well numerical simulation was established, which demonstrates that single-well EUR (estimated ultimate recovery) can be increased by 14.2% to 19.8% compared to gas wells without CO2 injection. This study provides essential guidance for the enhanced recovery in shale gas reservoirs through CO2 huff-n-puff.

Extended Laterals and Hydraulic Fracturing Redevelop Tight Fractured Carbonates

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216292, “Redevelopment of Tight Fractured Carbonates Through Extended Laterals and Hydraulic Fracturing,” by Antonio Buono, Cameron Taylor, and Alyssa Dordan, SPE, ExxonMobil, et al. The paper has not been peer reviewed. _ In the complete paper, the authors compare development scenarios in a fractured carbonate play between historic vertical and short horizontal development and modern hydraulically fractured extended lateral development. Because of its long production history and recent redevelopment efforts, the Austin Chalk was chosen as a natural laboratory to test how recent artificial stimulation techniques can lead to additional production from a wider range of pore systems. Development of the Austin Chalk In recent years, application of modern unconventional multistage hydraulic fracturing techniques, coupled with adding proppant to support induced fracture networks, mitigated the steep decline seen in historic production profiles. These improvements were exemplified in a recent Austin Chalk redevelopment where modern completions led to an increase in estimated ultimate recovery (EUR) by 250% on average. In the targeted area of development, historic, short Austin Chalk laterals without modern completions exhibited a wide range of well performance. Some outlier wells achieved high recoveries from accessing an existing natural fracture network with the original completion, whereas others, after only a few months of economic production, were unable to achieve continuous flow without a propped stimulation. The differences in performance partially can be explained by the fact that the reservoir quality of different intervals within the Austin Chalk is likely highly variable. This is exemplified in the B-2 Zone, which contains a well-developed vertical fracture network with variable lengths. Data suggest that the natural fractures are confined within the B-2 and it is geomechanically less-susceptible to wellbore collapse than zones with higher clay concentrations. The B-2 is likely a stiffer interval than the E Zone. This implies that differences exist in the properties of these units that are caused by changes in mineralogy and cementation. The authors aim to characterize reservoir quality and hydrocarbon distributions of the fractured relatively clean zones compared with the hydraulically stimulated reservoir in relatively “dirty” chalky zones, and evaluate geomechanical properties and production expectations from each zone. Depositionally, these units can be characterized broadly as carbonate-rich with subordinate siliciclastic detritus composed of clay minerals and silt-sized quartz and plagioclase grains. These units all contain distinctive stylolite seams. The most-favorable hydrocarbon shows typically are in the lower members of the Austin Chalk. Production data show that natural-fracture-only production (heritage) generally has lower total EUR with highly variable well-to-well production profiles vs. fracture and matrix production, which the authors write that they believe is achieved when unconventional technology is applied to these reservoirs. To project from heritage chalk production to expected EUR using modern completions, the authors used a risked EUR scaling factor of 1.5×. Consequently, the potential uplift in economic viability was recognized through a multiwell Austin Chalk appraisal to assess well-to-well communication with Eagle Ford codevelopment. The authors’ appraisal evaluated reservoir-quality differences between the main landing zones in the Austin Chalk with those from the E Zone of the Austin Chalk and Upper Eagle Ford, respectively, to demonstrate how significant economic uplift can be realized in mature, tight carbonate fields with unconventional technology.

Optimization method of fracturing fluid volume intensity for SRV fracturing technique in shale oil reservoir based on forced imbibition: A case study of well X-1 in Biyang Sag of Nanxiang Basin, China

An optimization method of fracturing fluid volume strength was introduced taking well X-1 in Biyang Sag of Nanxiang Basin as an example. The characteristic curves of capillary pressure and relative permeability were obtained from history matching between forced imbibition experimental data and core-scale reservoir simulation results and taken into a large scale reservoir model to mimic the forced imbibition behavior during the well shut-in period after fracturing. The optimization of the stimulated reservoir volume (SRV) fracturing fluid volume strength should meet the requirements of estimated ultimate recovery (EUR), increased oil recovery by forced imbibition and enhancement of formation pressure and the fluid volume strength of fracturing fluid should be controlled around a critical value to avoid either insufficiency of imbibition displacement caused by insufficient fluid amount or increase of costs and potential formation damage caused by excessive fluid amount. Reservoir simulation results showed that SRV fracturing fluid volume strength positively correlated with single-well EUR and an optimal fluid volume strength existed, above which the single-well EUR increase rate kept decreasing. An optimized increase of SRV fracturing fluid volume and shut-in time would effectively increase the formation pressure and enhance well production. Field test results of well X-1 proved the practicality of established optimization method of SRV fracturing fluid volume strength on significant enhancement of shale oil well production.

LOW SALINITY WATER INJECTION EVALUATION AND MATURE FIELD DEVELOPMENT AT ANGGORO SHALLOW SAND, SANGASANGA FIELD

AbstractAnggoro's shallow sand was perforated at well ANG-1033 (D4-N1 layer). Oil production in this well increased from an average of 15 BOPD to 170 BOPD. That the perforated layer was affected by low salinity water injection (salinity < 3000 ppm). Evaluation of water injection sweep efficiency was carried out using the Dykstra-Parson method, vertical efficiency of about 0.3, area efficiency of 0.7, and displacement efficiency of 0.3. The Estimated Ultimate Recovery (EUR) of this well provides is 197 MBbls with an additional RF of around 20 %. This increase is due to the low salinity water injection sweep mechanism that occurs. The clay content plays an important role in the mechanism that occurs in this layer, these mechanisms fines migration, increase in pH, multi ion exchange, double layer effect, and salting in which these mechanisms result in increased oil recovery. Seeing the production results from the D4-N1 layer and oil production in this layer can still be maximized, in the future this layer can be developed with several programs such as reactivation, workover, and development drilling.Keywords: Low salinity water injection, shallow layer, efficiency

Beetaloo or bust: the route to commercial success for an Australian shale play

There is a huge unconventional shale gas resource in the remote heart of the Northern Territory with the potential to trigger big new gas developments in Darwin, including export liquefied natural gas (LNG), and underpin the chronically undersupplied East Coast market. And yet despite this promise, the Beetaloo is not universally viewed as a viable commercial project. Nonetheless, the Beetaloo is one of the hottest topics in Australia’s upstream sector, and two of its key operators, Tamboran and Empire, are closing in on final investment decision (FID) for initial gas production. So why are many in the market still sceptical the play will work? To create a definitive answer, we investigate the key factors required to make the Beetaloo commercially viable. We will evaluate what a Beetaloo gas project could look like across a range of resource sizes, estimated ultimate recovery (EUR) from analogue production profiles, and well costs. The work aims to understand the key cost barriers and price hurdles that need to be broached for economic success. From here we can determine the full range of economic pitfalls and potential rewards that lie in wait for ambitious Beetaloo operators.

Beetaloo’s gas bounty: potential for LNG exports and domestic supply

Recent drilling in the Beetaloo Basin has yielded promising results for the emerging shale gas play, enabling the explorers to secure funding and establish a range of non-binding commercial agreements. Notably, Beetaloo wells have exhibited initial production rates over 30 days (IP30), spanning from 1.1 to 5.2 mmcf/day, and there is an evident trend of increasing frac stages with each new well as operators continuously optimise stimulation. This research paper presents comprehensive Beetaloo modelling results, encompassing well type curves, a comparative analysis with the renowned Marcellus shale play in the United States (frequently used as a reference point for Beetaloo), production forecasts, economic assessments, and an examination of potential commercialisation pathways. Based on our modelling findings, Beetaloo’s estimated ultimate recovery (EUR) per well ranges from 7 to 14 bcf, with deeper wells into the Velkerri B Shale demonstrating relatively higher productivity. We anticipate that, with full-scale development, Beetaloo could attain peak production rates of 1 bcf/day, supported by a peak annual drilling rate averaging 50–60 wells. Overall, the Beetaloo Basin possesses the potential to recover close to 8 tcf of low reservoir CO2 gas, opening doors to the possibility of liquefied natural gas (LNG) exports reaching up to 4.5 mtpa and domestic gas supplies of around 94 mmcf/day, limited to the existing infrastructure capacity connecting to the east coast gas markets.

Machine Learning-Based Research for Predicting Shale Gas Well Production

Machine Learning-Based Research for Predicting Shale Gas Well Production

Optimizing estimated ultimate recovery in horizontal wells through data-driven analytics

The oil and gas industry has undergone significant changes in completion operation design and execution, largely due to the advancements in machine learning techniques. In our study, we analyzed a dataset of approximately 200 horizontal wells from the Wolfcamp formations, categorized into two groups based on productivity and classification as parent versus child (infill) wells. Using response surface methodology (RSM), a data analytics technique that models the relationship between explanatory and response variables with up to second-order polynomial terms, we illustrate how to optimize horizontal well performance, specifically focusing on Estimated Ultimate Recovery (EUR). We selected a pool of 5 input variables for analysis, including initial oil production of 180 days, injected fluid volume in barrels, number of perforations in each cluster, volume of Hydrochloric Acid injected during frack job in gallons, and volume of injected Linear Gel in gallons.The selections were made in collaboration with industry professionals and validated using data analytics-based methods that rank variable importance. By pinpointing potential strategies to achieve a globally optimal EUR through the adjustment of completion variables (perforations and hydrochloric acid should be set at their data-recorded maximum values of 7.69 and 10.82 on a logarithmic scale), the study aims to simplify the challenging task of maximizing EUR from producing wells, offering an additional avenue for estimating reserves. It emphasizes the adaptability of this approach in allowing different completion designs to be dynamically tailored for optimizing EUR, each with its unique set of inputs. Ultimately, these diverse designs can be synthesized through meta-analysis. In summary, the study provides a practical framework for EUR estimation, readily applicable by operators in unconventional exploration, representing a transformative milestone in the industry. The subsequent phase involves collecting additional data from this specific field area and employing a range of models to improve precision and specificity.

Application of Machine Learning for Productivity Prediction in Tight Gas Reservoirs

Accurate well productivity prediction plays a significant role in formulating reservoir development plans. However, traditional well productivity prediction methods lack accuracy in tight gas reservoirs; therefore, this paper quantitatively evaluates the correlations between absolute open flow and the critical parameters for Linxing tight gas reservoirs through statistical analysis. Dominant control factors are obtained by considering reservoir engineering theories, and a novel machine learning-based well productivity prediction method is proposed for tight gas reservoirs. The adaptability of the productivity prediction model is assessed through machine learning and field data analysis. Combined with the typical decline curve analysis, the estimated ultimate recovery (EUR) of a single well in the tight gas reservoir is forecasted in an appropriate range. The results of the study include 10 parameters (such as gas saturation) identified as the dominant controlling factors for well productivity and geological factors that impact the productivity in this area compared to fracturing parameters. According to the prediction results of the three models, the R2 of Support Vector Regression (SVR), Back Propagation (BP), and Random Forest (RF) models are 0.72, 0.87, and 0.91, respectively. The results indicate that RF has a more accurate prediction. In addition, the RF model is more suitable for medium and high-production wells based on the actual field data. Based on this model, it is verified that the productivity of low-producing wells is affected by water production. This study confirms the model’s reliability and application value by predicting recoverable reserves for a single well.

Exploration history of the igneous reservoirs of the Rio Grande Valley (Mendoza), Neuquén Basin (Argentina)

Abstract Oil seeps related to igneous rocks in the Neuquén Basin have been known since pre-Hispanic times (sixteenth century) and have been explored in the southern Mendoza province since the late nineteenth century. In the 1980s, YPF (Yacimientos Petrolíferos Fiscales) began the exploration of igneous rocks as hydrocarbon reservoirs in the Río Grande Valley area. The possible productivity of these ‘unconventional’ reservoirs was recognized by studying outcrops and well data of sills and dykes emplaced in different formations of the fold and thrust belt of the northern Neuquén Basin. Mud-logging control and evaluations with drill stem tests (DST) were decisive to define these reservoirs as prospective. From petrographical reports on samples from outcrops and cores, six lithological types could be distinguished in the igneous units of this region. Recent works confirm that this volcanism belongs to two predominant cycles: from the late Oligocene to the Miocene (‘Molle’) and from the Middle to upper Miocene (‘Huincán’). Although in igneous reservoirs it is difficult to forecast the estimated ultimate recovery (EUR), sills that crosscut the source rocks of the Vaca Muerta and Agrio formations demonstrate surprisingly high production rates, although the number of wells for the complete development is always difficult to establish.

A New Method for Numerical Simulation of Coalbed Methane Pilot Horizontal Wells—Taking the Bowen Basin C Pilot Area in Australia as an Example

Coalbed methane (CBM) pilot wells typically exhibit a short production period, necessitating evaluation of their estimated ultimate recovery (EUR) through numerical simulation. Utilizing limited geological data from the pilot areas to finish history matching and subsequent production forecasting presents substantial challenges. This paper introduces a comprehensive numerical simulation workflow for CBM pilot wells, encompassing the following steps. Initially, geological parameters are categorized into two groups based on their statistical distribution trends: trend parameters (i.e., gas content, permeability, Langmuir volume, and Langmuir pressure) and non-trend parameters (i.e., fracture porosity, gas–water relative permeability, and rock compressibility). The probability method is employed to ascertain the probable high and low limits for trend parameter distributions, while empirical or analogous methods are applied to define the boundaries for non-trend parameters. Subsequently, the parameter sensitivity analysis is conducted to understand the influence of varying parameters on cumulative gas and water production. Conclusively, experimental design algorithms generate over 100 simulation cases using the identified sensitive parameters, from which the top ten optimal cases are chosen for EUR prediction. This workflow features two technological innovations: (1) considering the most comprehensive set of reservoir parameters for uncertainty and sensitivity analyses, and (2) considering the matching accuracy of both cumulative production and dynamic production trends when selecting optimal matching cases. This approach was successfully implemented in the C pilot area of the Bowen Basin, Australia. In addition, it offers valuable insights for numerical simulation of unconventional natural gases, such as shale gas.

Approach Maximizes Marginal Field Value by Going Back to Basics

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32379, “Marginal Field Value Maximization by Going Back to Basics With Design-to-Value Enhancement,” by Yen P. Chan, Muhammad Y.S.M. Yasin, and Ahgheelan S. Thurai, Petronas, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ Value is defined by a quantum of functions or returns received from resources invested. In extremely marginal oil and gas fields, returns hardly meet invested resources profitably. Stranded, widely scattered resources add to the complication. The complete paper shares an approach to notional well- and facility-development concept generation for such fields by shifting emphasis to designing for value to improve project viability rigorously. Problem Statement Based on Case Study Case study Field X, located offshore Asia, is one of many oil resources in the region with virgin reservoir pockets and layers that are marginal in volume yet can provide a considerable recoverable volume if developed collectively. However, being horizontally scattered and shallow, with a reservoir true vertical depth (TVD) of less than 2000 m, the field was designed initially with up to 12 wells to maximize reach, thus requiring a large wellhead platform (WHP) at the surface. Gas lift was required from the first day of production, leading to two pipelines at a minimum. Given the number of wells and surface facilities required, this led to a highly negative economic outcome. To quantify the extent of cost reductions and increase the recoverable targeted resources required to meet targeted economic indicators for a business case, the project team performed a series of sensitivity analyses. Despite efforts to phase out field development, minimize surface facilities, and simplify well design where possible, the project remained negative in net present value because reducing the number of wells and facilities will lead to a dramatically lower estimated ultimate recovery (EUR), which is the key driver in any oil and gas development. The project team thus was presented with an almost unworkable situation where scope and cost reduction to satisfy economic returns simply meant that installation of neither wells nor facilities was viable. The authors refer to the approach they describe in the complete paper as “going back to basics.” This approach, coupled with a design-to-value (D2V) approach, was effective in countering seemingly irreversible economic results. This approach is a systematic way to develop an optimal concept for maximum value realization because the agile approach questions every aspect of concept development to achieve minimum technical requirements while providing better clarity in cost/risk tradeoff through concept evaluation in a staircase manner. The process of executing D2V for marginal field development while managing technical risks is detailed in the following subsections for both well design and in facility-concept design.

Techno-economic integration evaluation in shale gas development based on ensemble learning

For the development of shale gas, the accurate prediction of estimated ultimate recovery (EUR) has invariably been a hot and arduous issue that has attracted abundant attention from researchers. However, the intricate relationship between EUR and economic benefits of shale gas wells is frequently disregarded. Therefore, based on the basic geological and engineering parameters, this study carried out a joint multi-task modeling of investment cost and EUR evaluation, and creatively constituted a techno-economic integration evaluation framework for shale gas wells with internal rate of return (IRR) as the economic benefit evaluation target. Furthermore, the interaction graphs of investment cost and EUR on IRR are delineated to intuitively exemplify the relationship between EUR and economic benefits (IRR). The validity of the model is verified by the field data from 231 wells. The results show that the techno-economic integration evaluation framework of Blendstacking, which integrates multi-task joint modeling and integrated learning, can reliably evaluate investment costs and EUR. Concurrently, based on the evaluation results, the accurate prediction of IRR is realized. The mean prediction errors of investment cost and EUR are inferior to 50 ×104USD and 1400 ×104m3, respectively, and the mean error of IRR is regulated within 2.0%. This work can quickly and effectively predict the economic benefits of gas wells under complex geological and engineering factors, which facilitates expeditiously developing decision making. The research method can be extended to the economic benefit evaluation of other instance well datasets.

Key geological factors governing sweet spots in the Wufeng-Longmaxi shales of the Sichuan Basin, China

Production performance of the Wufeng-Longmaxi shales varies significantly among Fuling, Weirong, and Wulong fields in the Sichuan Basin. Total organic carbon (TOC) content, mineralogy, and organic matter (OM) pore characteristics are investigated to identify key factors governing sweet spots. Siliceous shales with good preservation conditions in the Fuling Field exhibit large thickness, high TOC content and thin-section porosity (TSP), and well-developed OM macropores, thus high initial production and estimated ultimate recovery (EUR). Thin carbonate-containing siliceous shales with good preservation conditions in the Weirong Field feature medium-to-high TOC and well-developed OM macropores but low TSP, leading to high initial production but low EUR. Siliceous shales with poor preservation conditions in the Wulong Field are characterized by large thickness, high TOC, low TSP and poorly-developed OM macropores, causing low initial production and EUR.Both sedimentary and preservation conditions are intrinsic decisive factors of sweet spots, as they control the mineral composition, TOC, and OM macropore development. Deep-water shales in transgressive systems tracts (TSTs) exhibit better-developed OM macropores and greater TOC compared to highstand systems tracts (HSTs). OM macropores are most prevalent in siliceous shales, followed by carbonate-containing siliceous shales and then argillaceous shales. Furthermore, good preservation conditions are conducive to retain OM macropores with low pore aspect ratio (PAR). Comparison among the three fields shows that high-TOC silicious shales with good preservation conditions are the highest in TSP and EUR. Therefore, organic richness, lithofacies, and preservation conditions are the major factors which determine OM pore development, governing the sweet spots of the Wufeng-Longmaxi shales.

A New Approach to Apply Decline-Curve Analysis for Tight-Oil Reservoirs Producing Under Variable Pressure Conditions

Summary Decline-curve models inherently assume that the bottomhole flowing pressure (BHP) is constant. This is a poor assumption for many unconventional wells. For this reason, the application of decline-curve models might lead to incorrect flow regime identification and estimated ultimate recovery (EUR). This work presents a novel technique that combines variable BHP conditions with decline-curve models and compares its results with traditional decline-curve analysis (DCA) for both synthetic and tight-oil wells. Using superposition, we generate a synthetic rate example using the constant-pressure solution of the diffusivity equation for a slightly compressible fluid (decline-curve model) along with a BHP history. However, we validate the technique using bottomhole and initial reservoir pressures that contain errors. The algorithm consists of three sequential optimizations. In each optimization, the algorithm estimates (1) the decline-curve model parameters, (2) the BHP, and (3) the initial reservoir pressure. The result of the synthetic example leads to an accurate production history match and corrected estimates of the initial reservoir pressure and the BHP history. Finally, we compare the results of the technique with traditional DCA in terms of (a) the model parameters, (b) flow regime identification, (c) production history matches, and (d) EUR for tight-oil wells using three decline-curve models: 1D single-phase constant-pressure solution of the diffusivity equation for a slightly compressible fluid, logistic growth model, and Arps hyperbolic relation. For the synthetic case, the algorithm estimates the model parameters and the true initial reservoir pressure within a 2% error. In addition, the method regenerates the true BHP history and provides an excellent production history match. The analysis of the tight-oil wells shows that the new approach clearly identifies the flow regimes present in the well, which can be difficult to detect using traditional DCA when the BHP varies. In contrast, the application of traditional DCA shows considerable errors in the estimation of the model’s parameters and a poor history match of the production data. Finally, this work shows that incorporating variable BHP into the decline-curve models leads to more accurate production history matches and EUR values compared to using only rate-time data. This paper illustrates a workflow that incorporates variable BHP conditions for any decline-curve model. Moreover, the approach can handle errors in both the BHP and the initial reservoir pressure and provides corrected estimates of these variables. The technique is computationally fast and history matches and forecasts the production of unconventional wells more accurately than traditional DCA. The major contribution of this work is the remarkable simplicity yet robustness of our solution to variable-pressure DCA. Finally, we developed a web-based application to provide the readers with a hands-on experience of this new technique.

Fracture Conductivity Work Flow Models Well Spacing, Completions Design

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 2023-3864710, “A Comprehensive Simulation Study of Hydraulic Fracturing Test Site 2 (HFTS-2): Part I—Modeling Pressure-Dependent and Time-Dependent Fracture Conductivity in Fully Calibrated Fracture and Reservoir Models,” by Han Li, Jichao Han, SPE, and Jiasen Tan, SPE, Occidental Petroleum, et al. The paper has not been peer reviewed. _ Combining fracture and reservoir diagnostic analysis with integrated geomechanics and reservoir simulation is an efficient and cost-effective approach to generate realistic fracture geometry, understand fluid flow behavior, and define fracture-conductivity distribution in unconventional reservoirs. The complete paper presents a case study of integrated geomechanical and reservoir simulation with a developed fracture-conductivity-calculation work flow that was validated with diagnostic results to evaluate well spacing and completions design. Introduction This study extends that of previous authors by matching field fracture diagnostics and reservoir simulation using variable fracture conductivity. In the example used by the authors from the Hydraulic Fracturing Test Site 2 (HFTS-2) development, multiple fracture diagnostic methods were used to calibrate hydraulic fracture models. Once the model was calibrated, a new proppant-conductivity algorithm assigned conductivity values along the hydraulic fractures based on a physics-based model calculation of proppant concentration. Multiple mechanisms, such as stress- and pressure-dependent effects, time-dependent conductivity degradation, unpropped fractures, and proppant embedment, can all be considered in the novel fracture-conductivity-calculation methodology. Fracture-Conductivity-Calculation Work Flow The conductivity work flow developed by the authors uses simulated proppant concentration from a fracture model and experimental conductivity measurements of propped and unpropped fractures to define variable conductivity along hydraulic fractures. Conductivity measurements included sets of long-term (50-hour) experimental fracture-conductivity tests with various mesh sizes, proppant types, and closure stresses. The conductivity-calculation work flow developed by the authors was applied to the integrated simulation project of multifractured horizontal wells in the HFTS-2 project. Fig. 1 shows a flow chart of the work flow from fracture propagation modeling through integrated reservoir simulations. In general, the work flow consists of developing a 3D geological model, creating and calibrating parent wells’ hydraulic fracture models, calculating the fracture conductivity based on proppant concentration, history-matching the parent wells’ production and constraints with reservoir simulation, performing fracture-propagation modeling for child wells, and history matching and predicting the estimated ultimate recovery (EUR) for the entire pad. HFTS-2 Project Overview The HFTS-2 is in the Delaware Basin and is a cost-shared, field-based project designed to study hydraulic fracturing processes and production using state-of-the-art diagnostics, including fiber optic (FO) distributed temperature sensing and distributed acoustic sensing (DAS) pressure monitoring, image logs, cores through the fractures, and microseismic monitoring. Many studies have detailed the project overview of the HFTS-2, and this paper’s authors only include the overview of completions in the HFTS-2 project.