The initial high cost of exploitation of the sustained, increasingly growing development of unconventional resources in Argentina has resulted in concentrating all efforts to increase well productivity while reducing construction and completion costs. The optimization of hydraulic fracture (HF) treatments is vitally important. It is the primary strategy used to achieve an optimal reservoir drainage area, consequently characterizing the fracture geometry, including the height, for the continuous improvement of HF treatment and planning.
Several types of technologies and methodologies are used to estimate fracture height during and after a hydraulic stimulation treatment. These technologies can provide information about the fracture geometry and extension in the near-wellbore (NWB) and far-field areas. The determination of a reliable correlation between those methodologies represents a challenge as a result of formation complexity, heterogeneity, and limitations of evaluation technologies. It is well-known that some areas in the Vaca Muerta formation contain layers that can act as fracture barriers and are responsible for fracture containment.
This paper presents a fast and simple methodology that uses conventional well logs [gamma ray (GR), sonic, and density] from pilot wells to identify potential fracture barriers. This approach establishes a means to evaluate the degree to which the rock will have the ability to control fracture height growth. This methodology was determined useful for planning perforation intervals or clusters placement, particularly in those formations with stress profile showing reduced stress contrast and, when complemented with geological information, this method also provides useful information for horizontal well trajectory. Case studies are provided to illustrate examples of the proposed fracture barrier index (FBI) being calibrated or compared to other fracture height assessment. Additionally, the benefits of adding this new approach to current methodologies and technologies to aid completion design optimization and decision making is discussed.
The petro-elastic model (PEM) represents an integral component in the closed-loop calibration of integrated four-dimensional (4D) solutions incorporating time-lapse seismic, elastic and petrophysical rock property modeling, and reservoir simulation. Calibration of the reservoir simulation model is needed so that it is not only consistent with production history but also with the contemporaneous subsurface description as characterized by time-lapse seismic. The PEM requires dry rock properties in its description, which are typically derived from mechanical rock tests. In the absence of those mechanical tests, a small data challenge is posed, whereby all necessary data is not available but the value of reconciling seismic attributes to simulated production remains. A seismic inversion-constrained n-dimensional metaheuristic optimization technique is employed directly on three-dimensional (3D) geocellular arrays to determine elastic and density properties for the PEM embedded in the commercial reservoir simulator.
Ill-posed dry elastic and density property models are considered in a field case where the seismic inversion and petrophysical property model constrained by seismic inversion exist. An n-dimensional design optimization technique is implemented to determine the optimal solution of a multidimensional pseudo-objective function comprised of multidimensional design variables. This study investigates the execution of a modified particle swarm optimization (PSO) method combined with an exterior penalty function (EPF) with varied constraints. The proposed technique involves using n-dimensional design optimization to solve the pseudo-objective function comprised of the PSO and EPF given limited availability of constraints. In this work, an examination of heavily and reduced-order penalized metaheuristic optimization processes, where the design variables and optimal solution are derived from 3D arrays, is conducted so that constraint applicability is quantified. While the process is examined specifically for PEM, it can be applied to other data-limited modeling techniques.
A high risk of suboptimal well placement exists in new field development where seismic uncertainty can be great. Recent ultradeep resistivity measurement developments provide great benefits for identifying and optimizing the well path position within a given stratigraphic sequence. This paper presents a case study in which an operator planned to place wells 10 m TVD below the reservoir top because of seismic uncertainty of the top reservoir pick. To help mitigate this subsurface risk, the field development plan required real-time well placement optimization, using both standard formation evaluation data and an ultradeep azimuthal resistivity service. In this case-history, the ultradeep inversion canvases could be used to identify the well path position within the reservoir, as well as provide sufficient confidence to steer the well closer to the reservoir top than originally planned.
Multiple geological models, created from nearby offset wells and seismic grids, represented the expected seismic uncertainty of 5 to 15 m TVD. To identify the optimal measurement setup for real-time operations, resistivity modelling illustrated the effect of frequency and spacing on the data, producing multiple inversions for each geological scenario. After drilling began, real-time inversions for the ultradeep resistivity data were initially qualified using standard formation evaluation data, including both deep azimuthal resistivity and azimuthal density images. Multiple inversion canvases from various spacings and frequencies identified several formation features, including distances to the top and base of the reservoir. The quantified uncertainty of these results assisted in the evaluation of the inversion quality.
When close to the reservoir top, the wellbore position indicated in the ultradeep inversion canvases matched the interpretation from the conventional logs, which provided increased confidence in the inversion canvas results at distances farther away. This enhanced reservoir knowledge enabled the operator to progressively raise the well path to 5 and to 2 m TVD from the reservoir top. Except for strategic geosteering decisions based on expected faults positions from the seismic data, the operator made most well-placement decisions, across multiple wells, using ultradeep resistivity data. The high data quality and close collaboration within the subsurface team quickly led to high confidence in the inversion results. Integrating the full suite of available data, from shallow to ultradeep measurements in a comprehensive interpretation, provided better reservoir understanding, resulting in optimal well placement.
This paper presents formation evaluation results used within an integrated well-placement optimization service from a new field development. The integrated data qualified the results for an ultradeep resistivity tool. Confidence in the tool results enabled the operator to place wells much closer to the reservoir top than initially planned, in an area of seismic uncertainty.
The current cycle for reservoir management requires several months to years to update static and dynamic models as additional data from the field [logs, production, pressures, core, four-dimensional (4D), etc.] are obtained. These delays in updating the models result in increased risk and contribute to a significant loss of economic value. The ultimate goal for next-generation reservoir management is to reduce the cycle from several months to a few days. The current challenges for developing a proactive/real-time reservoir management solution include but are not limited to the time and manual intervention involved in conditioning and interpreting the logging-while-drilling (LWD) and well log data acquired during and after drilling a well; updating three-dimensional (3D) petrophysical/static models; and the computational cost and time involved in generating reservoir models from static and production data (history matching). However, the current widespread use of machine-learning and cloud-computing capabilities leads to faster and more accurate models, enabling real-time or near-real-time decision making. Using machine learning, one of these challenges—updating the 3D static models—was successfully addressed, namely, updating porosity prediction in a 3D model after new information comes to light, such as logging from a newly drilled well. The conventional geostatistical approach does not always honor geological variations in the subsurface formations, because only one or two seismic attributes can be used for co-simulation, and only with first-order interactions. Additionally, and most important, generating hundreds of realizations on a 3D grid is computationally intense and time consuming. Typically, several weeks are necessary to generate these static models before feeding them into the reservoir model. The proposed solution is a machine-learning-based approach that integrates 3D spatial availability of seismic data with petrophysical properties. One important goal of reservoir management is to understand reservoir uncertainties before they adversely affect field development. This machine learning solution proved to be computationally less costly, more accurate, and much faster than the conventional geostatistical approach.
Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources.
A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.
Using planar fracture models to match treatment pressure and improve understanding of the fracture geometry generation is not a new concept. Knowledge gained from this exercise has historically been used to improve engineered fracture completions and production, and maximize net present value (NPV); however, at some point during the progression from vertical to horizontal wellbores, many within the industry have forgotten about the learnings that can still be gained from current fracture models. Engineered completions have been largely replaced by spreadsheet efficiencies relevant to operations rather than production in too many cases. Some images of unconventional well stimulation treatments portray fractures growing in every direction, forming patterns that resemble shattered windshields, and have often excluded the known physics related to rock geomechanics, reservoir properties, and geology. Excuses to dismiss modeling are numerous and are gaining the reasoning of conformists.
Unconventional resource plays might or might not contain large numbers of natural fractures; but, current fracture models can still be used to gain insight into the fracture geometries being generated. While the development of complex fracture models continues to evolve, the industry can still gain insight to fracture geometry and resulting production using current planar fracture modeling. Caveats to this process are that it requires: Valid measured data to establish model constraints. The engineer to understand the basic physics of how fractures are generated and when (and when not) to twist the "knobs" in the model. The engineer to understand which "knobs" should be used based on real diagnostics information. The actual single well production to be an integral part of the process.
Valid measured data to establish model constraints.
The engineer to understand the basic physics of how fractures are generated and when (and when not) to twist the "knobs" in the model.
The engineer to understand which "knobs" should be used based on real diagnostics information.
The actual single well production to be an integral part of the process.
This paper demonstrates the results of honoring data measurements from a multitude of potential sources, including downhole microseismic data, downhole deformation tiltmeters, offset pressure monitoring, DTS, DAS, diagnostic fracture injection test (DFIT) analysis, injection as well as production data with bottomhole pressure measurements, etc., and the resulting observations and conclusions. Several industry examples are discussed to help frame the vast amount of information possible to help engineers do a better job of including more diagnostics into routine operations to provide additional insight and ultimately result in improved models and completion designs.
This paper is not intended to merely demonstrate the results of the work but to spark an interest in bringing more intense engineering back to fracture stimulation modeling for horizontal completions.
Abdulhadi, Muhammad (Dialog Group) | Tran, Toan Van (Dialog Group) | Chin, Hon Voon (Dialog Group) | Jacobs, Steve (Halliburton) | Suggust, Alister Albert (PETRONAS) | Usop, Mohammad Zulfiqar (PETRONAS) | Zamzuri, Dzulfahmi (PETRONAS) | Dolah, Khairul Arifin (PETRONAS) | Abdussalam, Khomeini (PETRONAS) | Munandai, Hasim (PETRONAS) | Yusop, Zainuddin (PETRONAS)
The first successful natural dump-flood in the Malaysian offshore environment provided numerous lessons learned to the operator. The minimal investment necessary for implementing the dump-flood coupled with the lack of recompletion opportunities in the subject wells suggested that direct execution without spending on expensive data gathering activity and extensive reservoir study makes more sense from a business point of view. A similar oil gain compared to a water injection project can be achieved at a significantly lower cost of USD 0.01 to 0.15 million in an offshore environment through dump-flooding.
The existing oil producers in the depleted reservoirs in Field B were originally completed and successfully drained oil from in a high-pressured watered-out reservoir below, making it an ideal dump-flood water source. The dump-flood was initiated by commingling the target and water source reservoir through zone change, allowing water to naturally cross-flow into the pressure depleted target reservoir. Once a memory production logging tool (MPLT) confirmed the cross-flow, the offtake well was monitored to determine the impact of the dump-flood and produce once the pressure was increased. Minimal investment was necessary because the operations were executed using slickline. The reservoir model will be calibrated once the positive impact of dump-flood is realized in the offtake well.
The first natural dump-flood in Reservoir X-2 has successfully produced 0.29 MMstb as of August 2018 with 600 BOPD incremental oil gain. The incremental recovery factor (RF) from the first dump-flood is predicted to be from 5 to 8%. Based on this success, it was decided to replicate the dump-flood project in other depleted reservoirs with Reservoir X-2 as an analog. Four reservoirs were subsequently identified, each with an estimated operational cost of approximately USD 0.01 million and potential incremental reserves of 0.10 to 0.20 MMstb per reservoir. The minimal investment necessary, the idle status of the wells and reservoirs, and the potential incremental reserves suggested that it is more appealing to proceed with implementing the dump-flood without undergoing an extensive and costly reservoir study. With reservoir connectivity being important to the success of dump-flooding, a more cost-effective approach would be to confirm the connectivity by monitoring the offtake well after the dump-flood is initiated. This approach provides more value because the cost of interference or pulse testing is significantly more expensive than the cost of the dump-flood itself while reservoir connectivity was already indicated as likely by geological data (map and seismic). Through a value driven approach, these dump-flood opportunities become more economically viable, allowing the operator to prolong the life of the assets and maximize the field profit.
This paper discusses using a value driven and business approach to implement the dump-flood in a mature field. Valuable insight into the business and technical considerations of implementing dump-floods are described, which are relevant to the industry, especially in today's low margin business climate.
Mogollón, J. L. (Halliburton) | Yomdo, S. (OIL India Limited) | Salazar, A. (Halliburton) | Dutta, R. (OIL India Limited) | Bobula, D. (Halliburton) | Dhodapkar, P. K. (OIL India Limited) | Lokandwala, T. (Halliburton) | Chandrasekar, V. (CMG)
The perception of better economics and less risk from infill drilling and recompletions are reasons well-focused remedies are preferred compared to reservoir-focused solutions, such as enhanced oil recovery (EOR). However, most literature does not discuss the economic and risk indicators driving this.
Using a real example, this work demonstrates that combining polymer flooding with infill drilling and recompletion substantially increases economic benefits with reasonable risk.
The reservoir considered is an Oligocene sandstone at a depth of 2700 m. The °API is 29.5 and permeability ranges from 50 to 500 mD. Current reservoir pressure is 43% of the original and it is below bubble point. A black oil model with a 133 × 56 × 128 grid was used. The model incorporated more than 50 years of matched primary and waterflooding production history and experimental polymer physico-chemical parameters. For the stochastic economic risks estimation, 1,000 iterations were run for each scenario considering uncertainties in injection-production, capital expenditures (CAPEX), operational expenditures (OPEX), and oil prices.
For a 20-year horizon, the injection-production-pressure profiles were numerically forecasted; economic results were calculated using a classic model and inputs from the forecast. The economic risk was determined stochastically. The redevelopment scenarios considered were as follows: Base: current waterflooding Existing wells interventions: workover, opening shut-in wells, and new perforations Infill drilling: vertical/horizontal infill drilling wells + existing wells operations Polymer flooding: using existing wells Combined Infill and polymer: vertical infill drilling wells and polymer flooding
Base: current waterflooding
Existing wells interventions: workover, opening shut-in wells, and new perforations
Infill drilling: vertical/horizontal infill drilling wells + existing wells operations
Polymer flooding: using existing wells
Combined Infill and polymer: vertical infill drilling wells and polymer flooding
P50 forecasts showed that interventions in existing wells in the base scenario increased oil production by 11% and net present value (NPV) by 71% with a risk index of 0.38.
A numerical optimizer was used to account for possible combinations of 14 potential drilling locations and vertical to horizontal well ratios. A scenario with three vertical wells was selected. Compared to the base case, this scenario showed an oil production increase of 23%, NPV increase of 178%, and a risk index of 0.41.
The injection rate of the polymer flood was optimized, resulting in a 17% increase in oil production and 95% increase in the NPV, with a risk index of 0.40. This justifies performing a polymer flood.
The most promising scenario is the combined infill drilling and polymer injection, which significantly improved the economic indicators—30% increase in oil production, 230% improvement of the NPV over the base scenario, with a risk index of only 0.41.
The results of this study demonstrate that the combination of EOR with different operational strategies results in significant benefits compared to the individual scenarios. Analysis of just oil production independent of economics and risk can be misleading. Infill drilling or flooding should no longer be the question. Instead, the question should be how they can be properly combined at various stages of asset life.
Inyang, Ubong (Halliburton) | Cortez-Montalvo, Janette (Halliburton) | Dusterhoft, Ron (Halliburton) | Apostolopoulou, Maria (University College London) | Striolo, Alberto (University College London) | Stamatakis, Michail (University College London)
Estimating the effective permeability and microfracture (MF) conductivity for unconventional reservoirs can be challenging; however, a new method for estimating using a stochastic approach is discussed. This new analysis method estimates matrix permeability and the unpropped and propped MF conductivities during laboratory testing where MFs were propped with ultrafine particles (UFPs).
Kinetic Monte Carlo (KMC) simulations form the basis of the method used to estimate effective permeability of the core sample. First, the stochastic model was implemented to calculate effective matrix permeability of a small core taken from unfractured Eagle Ford and Marcellus formation samples using scanning electron microscopy (SEM) images and adsorption data to obtain the pore-size distribution (PSD) within the sample. The KMC approach then evaluated the effect of various parameters influencing the conductivity of laboratory-created MFs. Case studies considered for this work investigate the conductivity improvement of a manmade MF as a function of the UFPs used as proppants that maintain width under high stress, the UFP (proppant) concentration, and the UFP flow perpendicular into a secondary or adjacent MF zone (2ndMF) penetrating the face of an opened MF during flow testing under stress. The leakoff area widths considered were 1, 2, and 3 mm and can be propped or unpropped.
Results obtained for the unfractured Eagle Ford and Marcellus samples closely correlate with other computational and experimental data available. For the laboratory-prepared nonpropped and propped MF samples, the effective propped width was determined to have the greatest effect on the MF conductivity, which increased by two orders of magnitude in the presence of the UFPs. The remaining two factors—proppant concentration and length of 2ndMFs—helped improve the effective MF conductivity in a linear manner; the highest proppant concentration and the 2ndMF zone resulted in the highest fracture conductivity achieved. Insight obtained from this study can be used to optimize fracturing designs by including UFPs and to create strategies for maximizing hydrocarbon recovery during development of unconventional resources where MFs are opened during stimulation treatments.
Abdulhadi, Muhammad (Dialog Group Berhad) | Tran, Toan Van (Dialog Group Berhad) | Chin, Hon Voon (Dialog Group Berhad) | Jacobs, Steve (Halliburton) | Wahid, Muhammad Izad Abdul (PETRONAS) | Usop, Mohammad Zulfiqar (PETRONAS) | Zamzuri, Dzulfahmi (PETRONAS) | Dolah, Khairul Arifin (PETRONAS) | Abdussalam, Khomeini (PETRONAS) | Munandai, Hasim (PETRONAS) | Yusop, Zainuddin (PETRONAS)
Infill Well B-23, which was recently drilled in the CIII-2 reservoir located in the Balingian Province, experienced a rapid pressure and production decline. The production decreased from 2,200 to 600 BLPD within 1 year. Analysis of the permanent downhole gauge (PDG) data revealed that Well B-23 production was actually influenced by two other wells, B-20 and B-18, each located 2,000 ft away. This paper discusses the ensuing analysis and optimization efforts that helped reverse the Well B-23 pressure decline and restored its production to 2,200 BLPD.
Based on the typical causes of rapid production and pressure decline, operators initially believed Well B-23 was located in a small, separate compartment compared to Wells B-18 and B-20. Additionally, the Well B-23 behavior differed significantly from Wells B-18 and B-20. PDG data analysis provided clear evidence of well interference despite the significant distance between the well locations. Changes in the other wells immediately affected the Well B-23 pressure, thus leading to the conclusion that production from Wells B-20 and B-18 impeded the pressure support for Well B-23. To optimize Well B-23 production, Well B-20 was shut in while Well B-18 was produced at a reduced rate because of a mechanical issue.
The optimization initially resulted in more than 500 BOPD incremental oil from Well B-23. The well pressure decline was reversed, with PDG data showing a continuous increase of bottomhole pressure (BHP) despite an increase in the production rate. Subsequently, production was fully restored from 600 to 2,200 BLPD, and reservoir pressure returned to its predrill pressure. Going forward, the optimum withdrawal rate from the CIII-2 reservoir will be determined to ensure maximum oil recovery from both Wells B-18 and B-23. The case study proved the significant benefit of PDG data, which helped identify well interference as the actual cause of the rapid decline in Well B-23, instead of a reservoir or geological issue. Through in-depth analysis and thorough understanding of the reservoir, the operator restored what initially appeared to be a poor well to full production.
This case study shows the clear and strong effect of well interference and highlights how the subsequent results of the optimization effort were rapidly obtained. A comprehensive understanding of the reservoir behavior could not have been achieved at minimum cost without the pair of PDGs installed. The analysis and lessons learned from the Well B-23 PDG data provide valuable insight regarding the impact of well completions to the field of reservoir engineering.