|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Crovetto, Carolina (Pan American Energy S.L.) | Moirano, Juan (Pan American Energy S.L.) | Vernengo, Luis (Pan American Energy S.L.) | Pellicer, Marcelo (Pan American Energy S.L.) | Duncan, Peter M. (MicroSeismic, Inc.) | Remington, Christine (MicroSeismic, Inc.) | McKenna, Jon (MicroSeismic, Inc.) | Khodabakhshnejad, Arman (MicroSeismic, Inc.) | Barker, William B. (MicroSeismic, Inc.)
Two horizontal wells were completed in Vaca Muerta Formation, Neuquén Basin, Argentina in June 2019. The hydraulic fracture treatments were monitored with a large surface microseismic array. The data acquired present several interesting observations about the results of the frac’ing and the interaction of the treatment with a nearby, previously completed producing well. This paper discusses the monitoring and the results achieved.
Lindero Atravesado field is located in the central part of Neuquén Basin, Argentina, in a geological region known as the "engulfment" (see Figure 1). To date more than 250 conventional and unconventional wells have been drilled in the field. Exploration and development activity in this particular field began in the late 60’s, with the first wells targeting Sierras Blancas and Quintuco formations.
The development of conventional oil and gas reservoirs in these formations began in the eastern sector of the field, moving lately towards the western sector. In 2000, the first deep well to Lajas and Punta Rosada tight gas sands was drilled and fractured in the eastern sector (Martínez et al., 2005). The encouraging results from this well led to the development of these unconventional reservoirs over the period of 2012 to 2018.
In 2012 the first two vertical exploratory wells were drilled to test the Vaca Muerta Formation as an unconventional shale oil reservoir. Logging, coring and microseismic monitoring of the hydraulic fracturing were done in both wells. A detailed reservoir characterization of the formation was carried out with these data as input to an integrated workflow centered on the simultaneous inversion of a 3D seismic dataset.
The results of the initial vertical wells and the detailed reservoir study led to the drilling of two horizontal wells in 2018. The wells were placed in the northern part of the field where the total organic carbon content and porosity seemed to be the most promising. Both wells were targeted to the lower part of Vaca Muerta Formation, at a depth where the proportion of organic matter, carbonates and silica favored both oil storage and successful hydraulic fracturing, informally known as "
Caprioglio, P. A. (Sinopec Argentina Exploration and Production, Inc.) | Jarque, G. (Sinopec Argentina Exploration and Production, Inc.) | Irigoyen, M. (Sinopec Argentina Exploration and Production, Inc.) | Maiztegui, G. (Sinopec Argentina Exploration and Production, Inc.) | Luz, N. (Sinopec Argentina Exploration and Production, Inc.) | D'Agostino, A. (Sinopec Argentina Exploration and Production, Inc.) | Casal, M. (Sinopec Argentina Exploration and Production, Inc.) | Villalba, D. (GeoLab Sur S.A.) | Villar, H. J. (GeoLab Sur S.A.)
Since the discovery of conventional oil in 1907, the Golfo San Jorge Basin (GSJB) has become into one of the most prolific hydrocarbon basins of Argentina, having an estimated areal extension of 180,000 km2. To this date, scarce information has been publicly released about unconventional exploration on its main hydrocarbon source rock, the lacustrine, Lower Cretaceous Pozo D-129 Formation (D129 Fm.).
The evaluation of the D129 Fm. for unconventional opportunities in El Huemul Field (EH) started in year 2013 and led to a recent shale oil discovery in well EHa-4301, which tested oil not only during completion but also began production under regular field operation conditions. Furthermore, shale-oil occurrence was also observed in well EH-4324
To understand organic richness distribution, kerogen type, hydrocarbon potential and thermal maturity windows of the D129 Fm. source rock in the area, 411 cutting samples obtained from 19 wells distributed along operated concessions were submitted to typical geochemical analysis that included TOC, programmed pyrolysis and organic petrography. Results show that current organic contents corresponding to the deep present structural position of the EH Field attain TOC values higher than 3% with the tightest cloud ranging between 0.8 and 2%. Type I lacustrine kerogen is predominant in the deepest facies of D129 Fm., while enrichment with Type III stands out in the transition to terrestrial-influenced facies. Thermal maturity spans a wide range from marginal-early stages of the oil window to late oil and transition to the gas-condensate stages.
Oils obtained from the D129 Fm. source rock in the EH Field show good states of preservation, contrary to conventional oils in the area, which typically depict evidences of biodegradation, rejuvenation and mixing processes that involve renewed migration pulses. In terms of physical characteristics, the produced shale oil exhibits API gravities around 44°, reflecting an outstanding quality in comparison with average conventional hydrocarbons of the basin.
In terms of potential, the D129 Fm. Upper Section constitutes a shale-oil target having an average thickness of 250 m. Next, the D129 Fm. Middle Section represents a hybrid system (interbedding of shale and tight) with potential for oil and gas production. Last, the D129 Fm. Lower Section seems to integrate two unconventional targets having different potential and is expected to be within the gas window. Adding up all three sections, the total oil window may even exceed 700 m of thickness for potential hydrocarbon production.
The paper will provide details around a relatively low-cost completion method, using a two packer mechanical straddle system, which enables in selectively treating a single stage or cluster, by ensuring isolation above and below the target zone thereby leading to a flawless execution of the designed treatment. This particular straddle system can be deployed on coiled tubing leading to an overall improvement in operational efficiency when being deployed and moving from one stage to another, hence saving money for a producer in the long run. This paper will describe the details and capabilities of the straddle tool, along with some specifics from successfully completed wells. This paper will also outline a data driven workflow developed to assess re-fracturing (refrac) candidate wells and the application of said workflow to a group of wells in an unconventional Permian formation, that were planned to be treated by an operator in the area. Finally, the paper will relay a simulation approach used to assess the effect of said treatment on long-term production. The refrac candidate well that will be referenced in the paper was drilled and completed in a tight formation. The approach employed here uses both data-driven and physics-based modelling when choosing the best refracturing candidate and can be applied to any group of wells. The approach assumes that the production and initial completion data is available for the wells studied in order to screen for potential candidates, and a reservoir model is also available for the chosen candidates for forecasting EUR upsides.
This paper discusses details of a mechanical straddle system, by highlighting the case histories of successfully pumping both proppant and acid stimulation systems. In addition, this paper also delves into the criteria (which employs the production and initial completion data) used to select the aforementioned candidate well as a candidate for refrac. Once the selection process is outlined, a hydraulic fracturing treatment design is shown for the well. Results from the fracturing design were then used as input in a history matched reservoir/completion simulation to assess long-term production as a result of the refrac design. The candidate well was filtered through the data-driven approach, providing a means for de-risking based on well history and then well performance was evaluated through fracture modeling and reservoir simulation.
The workflow provides a thorough methodology that allows an engineer to pick candidate wells for refracturing and assess the wells’ potential production post refrac treatment. The well in question was filtered through the data-driven criteria and once selected, production history was matched through simulation and sensitivities for refrac were conducted to show the effect on long-term production
Importance of combining a robust straddle technology, which is entirely manipulated mechanically, with the ability to choose the right candidate for refrac/restimulation provides a customer with an opportunity of improving their ROI.
In Argentina’s Neuquen Basin, Vaca Muerta Shale, EOR represents a significant opportunity due to the large resource in-place, low ultimate recovery factors from primary depletion, a substantial basin wide infrastructure, a tremendous subsurface data set, and a knowledge base that has evolved over the life of the field. The purpose of this work is to evaluate the effect of different EOR technologies to improve the initial rate and the ultimate recovery of Vaca Muerta shale oil horizontal wells. In particular, we have focused on enhanced recovery chemical cocktails that could be added during the hydraulic fracturing process to improve initial producing rates and ultimate recovery. As opposed to traditional enhanced recovery operations that take place later in the producing life of a well or reservoir, enhanced recovery during the initial fracture stimulation could likely lead to more attractive economics by improving early time production and avoiding the fill-up time required to replace produced fluids in a depleted drainage volume or reservoir. While we do not discuss specific chemical formulations as part of this paper, we do detail our approach to designing stimulation EOR treatments for a future pilot test and also show the potential for these treatments improving recovery and economics based on reservoir simulation models.
Assuming that well productivity is driven by spontaneous imbibition, our initial improved recovery investigations previous improved recovery efforts in the field focused on alternating water injection, but this strategy proved unsuccessful as capillary pressure hysteresis drives this mechanism. Following these disappointing results, we started studying the Vaca Muerta from a rock microstructure standpoint. The microstructure variations in the Vaca Muerta we much more significant than those of conventional reservoirs. Often times, the microstructure varied widely at the scale of a few millimeters and many significant changes could be observed across a single core plug. This significant variation in microstructure is likely explained by the longer depositional time span of a shale reservoir depositional environment when compared to most conventional reservoir depositinal environments.
The Vaca Muerta shale has been long regarded as a water-wet shale because of the low percentage of frac water recovered during well production. We identified intercalations of possibly massive water-wet zones and strongly oil-wet zones in the Vaca Muerta kitchen zone. The oil-wet intercalations have high porosity and adsorption isotherms that could indicate more permeability than the water-wet zone. The water-wet intercalations are highly saturated with water, and similarly, the oil-wet intercalations are highly saturated with oil. The laboratory protocol indicates a large percentage of macro and meso-pores. The decane size is 0.7 nm. Thus, the liquid phase is present in pore sizes above 1 nm and above.
The goals of our EOR cocktail design were to make favorable changes in interfacial tension, viscosity, and wettability. Intercalations of high porosity high permeability zones in which the injection of a mutual solvent that reduces viscosity could also change wettability in oil-wet/water-wet Vaca Muerta, improving matrix connectivity and initial oil rate.
Lab results and reservoir simulation show that these changes alone could increase initial oil rates by 20%. Because Vaca Muerta’s organic porosity is a weak point in the rock fabric, which turns out to be the entry zones for chemicals, it is possible that the injected chemicals could also induce a connectivity cascading effect that could increase permeability and further increase the initial rates above those percentages.
Using the results from our laboratory and reservoir simulation studies, a four-well field pilot test has been designed. In this future pilot, we will test different injection concentrations, while keeping the total mass constant. In this manner, we will estimate the volume contacted by the chemicals.
Bakar, Afdzal Hizamal Abu (PETRONAS) | Jamaluddin, Muhamad Nasri (PETRONAS) | Musa, Rizwan (PETRONAS) | Din, Azmi B (PETRONAS) | Badawy, Khaled (PETRONAS) | Salleh, Salhizan Mohamad (PETRONAS) | Shabarudin, Ezza Shazana M (PETRONAS) | Fuenmayor, Roberto (Schlumberger) | Trivedi, Rajesh (Schlumberger) | Mokhlis, Mohamad Mustaqim (Schlumberger) | Tri, Nghia Vo (Schlumberger) | Hermann, Roland (Schlumberger) | Hassan, Muhammad Firdaus (Schlumberger) | Harith, Mikhail (Schlumberger)
As the industry had become more challenging because of the lower oil price and the competitive environment during the last decade, businesses are demanding the organization to be agile, reduce costs and integrate technologies, people and processes to improved efficiency and sustain production.
PETRONAS aspire to be proactive and rapid deciding on how to sustain day-to-day production through developing multiple workflows to handle different scenarios. These workflows include Gas Lift Surveillance, Optimization and Design, Well Test Validation and Sand Management.
By deploying proper instrumentation at the fields, automated data processing and faster well and network model updates have helped PETRONAS to identify candidates in a timely manner as compared to the traditional manual way. The automated workflows had helps on reducing the screening time, however the next challenge is the interdependency posed by the results from each workflow.
The production surveillance engineer still requires performing subsequent technical analysis to connect multiple aspects of the validation before the candidate can move forward as final recommendation and execution.
These challenges gave birth to a new opportunity to do a composite insight on how pieces of multiple solutions and integrate into a singular system to obtain a higher confidence level of the success of those candidates.
To efficiently manage this challenge at scale, Digital Fields is deployed as the unified production platform for PETRONAS Upstream. Digital Fields provides a user experience to all assets with the following characteristics:
• Scalable to all PETRONAS fields operated in Malaysia and International assets
• Rapid development
• Common company-wide platform
This paper will discuss on The Optimization Advisory solution that was deployed in Digital Fields to all fields in 2019 and created additional benefits to the organization as it helped production surveillance engineer and production technologist with:
• Transparency on the potential candidates
• Higher confidence on candidates to be executed in the field
• Standardizing practices across all PETRONAS oil fields in Malaysia
This study uses analytical models to examine the independent impacts of proppant and fluid on Middle Bakken child well production. Parent wells were defined as having no neighbors within 3000 feet at the time of completion +180 days. A ‘spacing factor’ for the remaining (child) wells was calculated by combining well spacing, producing days, and coverage (‘boundedness’), differentiating less-impacted and more-impacted child wells. Parent wells were assigned an arbitrary spacing factor, allowing their ‘spacing’ to be quantitatively compared to child wells.
Non-linear regressions were performed for each well class to predict five-year production from eight input variables including well log properties and reservoir, drilling, and completion data. The models allowed inspection of variables’ impact on each well class, but for all classes production correlated strongly negatively to water cut and positively to lateral length, proppant-per-foot, and resistivity. In child wells, proppant-per-foot and depth were more important than in parent wells. Fluid-per-foot was of relatively low importance in all models.
Focusing on the more-impacted child wells — since child locations are generally the ones that remain to be drilled — the regression model was then re-built using four sub-classes for completion type. In addition to water cut, three other variables — lateral length, proppant-per-foot, and depth — were all weighty contributors to high production. Fluid-per-foot remained a minor predictor of production, possibly because of its close correlation to proppant-per-foot. This model was used for simulations with various combinations of proppant-per-foot and fluid-per-foot, with the resulting production predictions pointing toward optimal completion practices.
The spacing factor used in classifying child wells is novel in its combination of physical spacing, producing days, and boundedness, and in its incorporation of parent wells, which are technically unbounded. The multidisciplinary analytical approach used here can serve as a template or an example of asset optimization, allowing for physical conclusions that quantify the impacts of different engineering choices. Predictive modeling (simulation) highlights fluid and proppant designs that optimize production.
These insights into the interplay and relative significance of geologic & engineering variables could help focus child well development to maximize geologic advantages as well.
Production shut-in is common for all E&P operators. Well shut-in could be due to planned surface or downhole maintenance, unplanned equipment failures, or in response to offtake constraints. Industry so far has not developed a common understanding on whether frequent/high downtime percentage has a long-term impact on well production. This work has identified a group of wells that experienced high downtime due to unplanned surface constraints and attempted to answer the question whether an operator should shut-in or choke back wells in response to surface constraints. In general, it is assumed that there is no long-term production impact rather than deferred production loss from individual well shut-in. However, this assumption may not be valid. This study analyzes production performance of unconventional wells with different downtime scenarios. A machine learning auto forecast algorithm based on SPEE monograph 4 was developed and used to estimate ultimate recovery (EUR) forecasts for multiple completions vintages of more than 2000+ wells. The change in EUR and key parameters like decline rate are studied to assess whether statistically significant correlation to percentage of downtime exists. This work indicates potential for reduced EUR with long shut-in. In addition, shut-in early in the life of the well appears to be more detrimental than shut-in at later time.
The crude price fluctuation in early 2020 has led to a lot of discussion and actions with the industry. The oil and gas companies are required to re-think their field management strategy in response to low oil price, not only with the drilling and completion, but also on the current producing wells. Due to the limited storage, pipeline exporting capacity, and high operating cost for older wells, it is inevitable for operators to shut in wells. For example, in the Bakken field, a significant portion of wells have been shut-in since second quarter (EIA, 2020). A natural question is what kind of impact from this long term shut-in has on short-term and long-term well performance.
Comprehensive analysis of the impact of well shut-in unconventional reservoir fields is limited. SPE Reservoir Advisory Committee (SPE RAC, 2020) provided a high-level overview based on historical perspectives. Most conventional reservoirs experience short shut-ins, and potentially see benefit from long-term shut-ins due to re-pressurization of the field and stabilization of the fluid contacts (Dujardin et al., 2011). This follows theoretical expectations; however, empirical confirmation is sometimes limited since most wells lack pressure monitoring during long term shut-in to provide data for analysis. This kind of data is even more sparse for unconventional wells. Because of extremely low permeability, a buildup analysis that can shows flow regime patten typically requires more than 6 months shut-in (Azari et al., 2018), which is generally not desired by operator. Instead, analysis relies on production data or theoretical basis.
Historically the evaluation of unconventional reservoirs dominantly relied on analytical methods like Decline Curve Analysis (DCA) and Rate Transient Analysis (RTA). Although they are effective and convenient, the lack of fundamental understanding such as fracture geometry leads to high uncertainties in analysis and consequently challenges in improving the accuracy of forecast or explaining the production mechanism.
A geomechanical integrated reservoir modeling approach is developed to precisely tackle this limitation. Because of the extremely low permeability and widely utilized horizontal drilling with multiple hydraulic fracture design, a conventional modeling approach could not be adopted directly without the critical addition of geomechanical workflow. The complete approach includes 1) Geomodelling 2) RTA and hydraulic fracture modeling 3) graphic processing unit (GPU) based reservoir simulations and 4) Poro-elastic impacts on stress modeling. The change of each portion will impact the whole, therefore a workflow with fewer tools and iterations greatly benefits the efficiency of the methodology. A single tool including fracture modeling and dual-porosity simulation were deployed and successfully demonstrated the power of this process in business operation.
A few field examples are used to demonstrate this approach. They include the history matching (HM), optimization of completion and spacing strategy for 1) a dry gas field 2) a black oil field and 3) a workflow for new entry field with almost no data. The lack of lab measurements (such as SCAL, k, sigma) are common and the assumptions used are elaborated. It is important to recognize simulation uncertainties, So the hypothesis has tested by utilizing modeling design from pilot wells.
Although almost everyone in the industry recognizes the importance of integrated modeling, practical application has been very challenging. The coupling of geomechanics and reservoir simulation typically not only requires extremely long cycle times to deliver, but also relies on significant computational power. Therefore, analysis of different realizations is generally not feasible, and appropriate uncertainties are not captured. The presented approach illustrated as an integrated method using one tool which successfully reduced the cycle time of pad level modeling to weeks, therefore improved the efficiency of reservoir engineers significantly.
Romero, Adriana (YPF Tecnología) | Feldmann, Christopher (YPF S.A.) | Alonso, Katherine Silva (YPF S.A.) | Martinez, Gustavo (YPF S.A.) | Barros, José (YPF S.A.) | Montero, Marcelo (YPF S.A.) | Claramunt, Juan Ignacio Alvarez (YPF S.A.) | Ferrigno, Eugenio
The main objective of this work is to identify and enable automatic online optimization actions in wells with plunger lift systems using machine learning.
For this purpose, a fault classification model has been developed for plunger lift systems using neural networks focused on production loss detection.
With the use of this tool, the aim is to reduce 10% of undetected production losses and increase production by 10%, improve the allocation of resources in the field, reduce the intervention of slickline equipment and improve the opportunities for multidisciplinary work between production engineers, data science specialists and automation specialists, so that an integrated solution can be obtained using data physics, prescriptive analysis and automation models.
The conceptualization of this proposal started by defining the different types of failure that are responsible for production losses or optimization opportunities applicable to wells with Plunger Lift. Time series techniques were used to compare and isolate failure patterns available in historical head pressure data. The patterns were subsequently converted to images and those images corresponding to failures later classified using neural networks. Finally, an interdisciplinary team of data scientists, production engineers, and optimizers, identified and proposed the actions to solve each group of failures.
3000 images corresponding to failures were collected and used to train the classifier, obtaining an accuracy of 80% in the identification of new events.
Due to the large number of wells in the field (around 400), this classifier has been built on the principle of wells performance control by handling through exception, as otherwise it would unpractical to analyze the totality of events occurring daily. Thus, this tool has proved to be of great help in reducing study times by focusing primary on those wells that are impairing production and later on those which might still be optimized, aiming at the end of the process achieving operational excellence by maximizing production across all wells to their expected potential.
The project also highlights YPF′s leading business approach by promoting the concept of multidisciplinary collaborative spaces by merging specialists from Experience Center Data Scientist and production Engineering Sector. This convergence provides a considerable improvement to an economical and profitable extraction system for unconventional wells.
The identification of failures in the Plunger Lift extraction system presented in this work consists of an extension of information that is already in the literature, however, the use of machine data driven models coupled with a classifier tool which optimizes actions using a risk approach based on production losses to identify and solve these types of failures is considered a novel instrument. Also, this work is the initial part of a slickline schedule automation system.
The success of an unconventional hydrocarbon development depends on effective stimulation of reservoir rocks maintaining well integrity during stimulation and production especially for the Vaca Muerta. While key decisions such as well spacing and completion intensity greatly impact the economics of the asset development, well integrity remains as the key risk that must be mitigated to warrant effective stimulation of the formation. In cooperation with SHELL [Bai 2016, Yeh 2018] we have developed a coupled geomechanical and reservoir modeling workflow that can address the interplay of well spacing and completion intensity, while mitigating the risk of casing deformation.
Integrated geomechanical and reservoir modeling incorporates a range of multi-disciplinary inputs such as the layered geologic model, well drilling and landing zone, completion design, operational management strategies, and production performance. In a first step the hydraulic fracturing model is calibrated to best available field data. After sufficient calibration quality is reached the forecast quality at neighboring wells is verified. Then the model is used in the forecast mode to optimize well spacing for single or multi-layered field development, including optimal well landing zone identification and completion design that attempts to maximize Net Profite Value (NPV) and Estimated Ultimate Recovery (EUR), while also predicting the potential magnitude and location of bedding plane shear induced casing deformation events.
The results provide critical inputs for decisions on well spacing, well landing and completion designs with reduced number of field trials for achieving optimal operational conditions. In addition, the workflow provides valuable insights for critical data acquisition to evaluate and forecast field performance.
To address the industry-wide challenge to increase predictability and confidence in numerical models an appropriate characterization of rock heterogeneity and handling of uncertainties of subsurface parameters is crucial. These two critical functionalities are addressed with the developed technology. Firstly, we capture the subsurface layering heterogeneity, i.e. thickness and spatial frequency of shale, carbonate and ash layer occurrence, along with natural fractures or planes of weakness with a truly 3D anisotropic modeling approach. Secondly, we balance the estimation of uncertainty ranges in model input parameters with the ability to converge to a calibrated geomechanical model with sufficient forecast quality for the evolution of the fracture network and resulting hydrocarbon production. After the successful calibration,this technology has shown to be appropriate for the accuracy of prediction of well EUR outside the calibrated conditions and is today standard approach for hydraulic fracture modeling in SHELL [Bai 2016].
A case study with TOTAL [Pourpak 2019] is presented in this paper to demonstrate the effective way of incorporating subsurface heterogeneity and variability into hydraulic fracture models to achieve a calibrated reservoir model and to use that model in the forecast mode to predict EUR along with all costs and NPV for a large window of variability of operational parameter.
In addition, the risk of shear deformation along bedding planes, which is believed to be one of the main mechanisms for casing deformations and well integrity issues, is investigated and measured These quantifications of casing deformation risk are used today as constraint in the optimization process to achieve profitable production and minimization of casing deformation at the same time.