A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.
Identification of tidal channels fairways is key for predicting behavior of areas at higher risk to water breakthrough or otherwise have a significant impact on the development and monitoring of reservoir performance. However, tidal channels in carbonates are not often easily characterized using conventional seismic attributes. It is important to decipher the complexity of the carbonate tidal channel architecture with integrated multisource data and a variety of approaches.
In this paper, petrological characteristics and petrographic analysis is conducted on well logs and validated carefully using core data. Then, the second step is to compare the carbonate channel systems with modern analogue in Bahama tidal flat and outcrop scales in Wadi Mi'Aidin (Northern Oman). Thereafter, the supervised probabilistic neural network (PNN) and linear regression method were undertaken to detect an additional channel distribution.
The relationship of high porosity with low acoustic impedance appeared mostly in the channel facies which reflects good reservoir quality grainstone channels. Outside these channels, the rock is heavily mud filled by peritidal carbonates and characterized by a high acoustic impedance anomaly with low quality of porosity distribution. The new observation of PNN porosity volume revealed a lateral distribution of the Mishrif carbonate tidal channels in terms of paleocurrent direction and the connectivity. Additionally, the prior information from core data and the geological knowledge indicate a good consistency with classified lithology. These observations implied that Mishrif channels consist of a wide range of lithology and porotype fluctuations due to the impact of depositional environment.
The work enables us to provide a new insight into the distribution of channel bodies, and petrophysical properties with quantification of their influence on dynamic reservoir behavior of the main producing reservoir. This work will not only provide an important guidance to the development and production of this case study, however also deliver an integrated work path for the similar geological and sedimentary environment in the nearby oil fields of Southern Iraq.
The objective of this work is the prediction of water salinity evolution trend for Mexico Area-1 development that foresees the injection of a mixture of seawater and produced water from the six different reservoirs connected to the same FPSO.
Prediction of salinity trend evolution is crucial for forecasting possible biogenic hydrogen sulphide (H2S) formation and foreseeing the relating impacts over completion and facility material selection and on health, safety and environment (HSE) management.
Traditional numerical simulations through stand-alone models do not consider the effects of the reciprocal interaction among the fields on production profiles and are not able to simulate salinity evolution of produced and injected water mixture, variable over time. To overcome this limit, a new tool was developed. It consists in a python script that, introduced into the Area-1 Integrated Asset Model, allowed to generate forecasts of the water salinity along the project lifetime. These simulations were essential for souring risk assessment, providing the following results: water salinity trend evolution at each injector well; water salinity trend evolution at each producer well; injection water breakthrough timing at the producer wells.
water salinity trend evolution at each injector well;
water salinity trend evolution at each producer well;
injection water breakthrough timing at the producer wells.
Moreover, it gave the opportunity to assess the injection strategy efficiency and to quantify the impact of changing salinity on water viscosity and on the field recovery.
In conclusion, the innovative methodology applied in the Area-1 IAM (Integrated Asset Model) permits to predict the salinity of injected water and to foresee salinity evolution of produced water generating several valuable information, providing a flexible tool that allows to investigate simultaneously several uncertainties related to the project and to evaluate promptly solutions and mitigation.
Moreover, when the reservoirs will be on production, the numerical models integrated with the developed script will reproduce the historical salinity data allowing to identify preferential flow path established by fluids virtually acting as a reservoir tracer technology.
With the advent of high-resolution methods to predict hydraulic fracture geometry and subsequent production forecasting, characterization of productive shale volume and evaluating completion design economics through science-based forward modeling becomes possible. However, operationalizing a simulation-based workflow to optimize design to keep up with the field operation schedule remains the biggest challenge owing to the slow model-to-design turnaround cycle. The objective of this project is to apply the ensemble learning-based model concept to this issue and, for the purpose of completion design, we summarize the numerical-model-centric unconventional workflow as a process that ultimately models production from a well pad (of multiple horizontal laterals) as a function of completion design parameters. After the development and validation and analysis of the surrogate model is completed, the model can be used in the predictive mode to respond to the "what if" questions that are raised by the reservoir/completion management team.
Standard approaches to optimization under uncertainty in reservoir simulation require use of multiple realizations, with variable parameters representing operational constraints and actions as well as uncertain scenarios. We will show how appropriate use of local optimization within the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations.
To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different types of decision. Investment decisions, such as facilities sizing, depend on global unknowns and must be optimized for the complete ensemble. Operational actions, such as closing a valve, can be optimized instantaneously for individual scenarios, using measurable information, although subject to constraints determined at a global level. In this study, we implement local optimization procedures within simulation cases, combining customized objective criteria to rank reactive or proactive actions, with the ability to query reservoir flow entities at appropriate frequencies.
The methods presented in the paper can be used for reactive response modeling for smart downhole control; optimization of ESP/PCP pump performance; and implementation of production plans subject to defined downstream limits. For selected cases, we compare the advantages and disadvantages of the local optimization approach with standardized "big-loop" uncertainty workflows. The methodology can significantly reduce optimization costs, particularly for high-frequency actions, achieving similar objective function values in a fraction of the time needed for post-processing optimizers. Use of tailored scripting provides the capability to modernize the logic framework for field management decisions, with realistic representation of smart field equipment and flow entities at any level of complexity.
Use of efficient workflows as described in this paper can reduce the cost of multiple realization studies significantly, or enable engineers to consider a wider range of possible scenarios, for deeper understanding and better risk mitigation.
Masini, Cristian (Petroleum Development Oman) | Al Shuaili, Khalid Said (Petroleum Development Oman) | Kuzmichev, Dmitry (Leap Energy) | Mironenko, Yulia (Leap Energy) | Majidaie, Saeed (Formerly with Leap Energy) | Buoy, Rina (Formerly with Leap Energy) | Alessio, Laurent Didier (Leap Energy) | Malakhov, Denis (Target Oilfield Services) | Ryzhov, Sergey (Formerly with Target Oilfield Services) | Postuma, Willem (Target Oilfield Services)
Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust.
A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks.
In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy.
The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched.
Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming.
In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.
Development of oil rim reservoirs is challenging and could lead to low oil recovery, if multiple determining factors are not well understood, that influences successful field development concept. It requires detailed analysis and development of specific procedures to optimize the oil production from a thin oil rim underlaying gas cap. Few IOR/EOR applications for oil rim development have been reported in the literature so far. This study presents a concept for the optimization of oil production from an oil rim reservoir by numerical simulation.
As a starting point, a representative sector of the field was selected for the initial analysis. It was decided to perform IOR/EOR methods including water/gas flooding/injection and surfactant flooding using inverted five-spot horizontal well pattern, for the application in the selected sector. Upon execution of the detailed sensitivity analysis, the pattern was optimized by its characteristic geometric variables including the length of the vertical/horizontal section of the well, the location of the wells, lateral well distances and the orientation of the pattern. The optimization was performed by setting an objective function to improve recovery factor and reduce water/gas cut by using the differential evolution algorithm. The latter was run until converging, and the optimal solution was used to perform further IOR/EOR studies.
Finally, after selection of a base-case scenario and best well pattern, IOR/EOR options were evaluated, and the comparative results were reported. The generated results show that the application of 5-spot horizontal well pattern in the oil rim reservoir could increase the oil recovery by water flooding, but with low sweep efficiency. The losses of injected water into the underlaying aquifer and up laying gas gap are large. Immiscible gas injection into the gas cap can support the pressure but massively increases the gas cut. In addition, displacement efficiency by gas flooding is poor.
Simulation results of the surfactant flooding case shows better displacement efficiency compared to water flooding. Also, the possibility of reducing residual oil saturation could increase the ultimate oil recovery but at very late time.
This study examines which is the margin of usability for Artificial Intelligence (AI) algorithms related to the rock properties distribution in static modeling. This novel method shows a forward modeling approach using neural networks and genetic algorithms to optimize correlation patterns among seismic traces of stack volumes and well rock properties. Once a set of nonlinear functions is optimized in the well locations, to correlate seismic traces and rock properties, spatial response is estimated using the seismic volume. This seismic characterization process is directly dependent on the error minimization during the structural seismic interpretation process, as well as, honoring the structural complexity while modeling. Previous points are key elements to obtain an adequate correlation between well data and seismic traces. The joint mechanism of neural networks and genetic algorithms globally optimize the nonlinear functions and its parameters to minimize the cost function. Estimated objective function correlates well rock properties with seismic stack data. This mechanism is applied to real data, within a high structural complexity and several wells. As an output, calibrated petrophysical time volumes in the interval of interest are obtained. Properties are used initially to generate a geological facies model. Subsequently, facies and seismic properties are used for the three-dimensional distribution of petrophysical properties such as: rock type, porosity, clay volume and permeability. Therefore, artificial intelligence algorithms can be widely exploited for uncertainty reduction within the rock property spatial estimation.
Tavassoli, Shayan (The University of Texas at Austin) | Krishnamurthy, Prasanna (The University of Texas at Austin) | Beckham, Emily (The University of Texas at Austin) | Meckel, Tip (The University of Texas at Austin) | Sepehrnoori, Kamy (The University of Texas at Austin)
Storage of large amounts of CO2 within deep underground aquifers has great potential for long-term mitigation of climate change. The U.S. Gulf Coast is an attractive target for CO2 storage because of the favorable formation properties for injection and containment of CO2. Deltaic formations are one of the primary targeted depositional environments in the Gulf Coast. This paper investigates CO2 storage in deltaic saline aquifers through a combination of geological modeling and flow simulation.
The geological model in our study is developed based on a laboratory-scale 3D flume experiment replicating the formation of a delta structure and populated with geologic properties according to Miocene Gulf of Mexico natural analogues. We used invasion percolation simulations to understand the gravity- driven flow and the relationship between architecture, stratigraphy, and fluid migration pathways. The results were used to develop an upscaled model for compositional simulation with the key features of the original geological model and to determine injection schemes that maximize the injection capacity and minimize the amount of mobile CO2 in the formation. In order to achieve this, we used compositional reservoir simulations to study the pressure-driven flow and phase behavior.
The results of invasion percolation simulations were used to identify the key stratigraphic units affecting CO2 migration. The realistic geometries and high resolution of the model facilitate the transfer of results from synthetic to subsurface data. The results allow for the analysis of deltaic depositional environments, important stratigraphic surfaces, and their impact on CO2 storage. The reservoir simulation model and phase behavior were validated against available field and lab data. The results of reservoir simulations were used to investigate the effects of main mechanisms, such as gas trapping and solubilization, on storage capacity. We compared our simulation results on the basis of invasion percolation (gravity driven) and reservoir simulation (pressure driven). The comparison is helpful to understand the strengths and weaknesses of each approach and determine best practices to evaluate CO2 migration within similar formations.
The unique and extremely well characterized deltaic model allows for unprecedented representation of the depositional aquifer architecture. This research combines geologic modeling, flow simulation, and application for CO2 storage. The integrated conclusions will constrain predictions of actual subsurface flow performance and CO2 storage capacity in deltaic systems, while identifying potential risks and primary stratigraphic migration pathways. This research gives insights on prediction of CO2 storage performance and characterization of prospective saline aquifers.
This challenging reservoir characterization case study is defined by the interaction between two reservoirs with different production mechanisms: a fractured basement reservoir and an overlying sandstone reservoir. The existing static geologic concept has been significantly enhanced by integrating pressure data from a unique three-year shut-in period to aid modeling of fractured reservoir connectivity. Previously, the seismic dataset was predominantly used to model the fault and fracture network and guide well planning. In the current approach, the full field data set, including all drilling parameters and new reservoir surveillance data were integrated to address uncertainty in the connected hydrocarbon volume and the relative importance of each production mechanism. The result is a reservoir management tool with which to test re-development concepts and effectively manage pressure decline and increasing gas/oil ratio (GOR) and water production.
To achieve a fully integrated history matched model, the first step was to make a thorough review of the existing detailed seismic interpretation, vintage production logging tool runs (PLT's), wireline logs (including borehole image logs (BHI)) and drilling data to find a causal link between hydraulically conductive fractures and well production behavior. In parallel, a material balance exercise was run to incorporate the new pressure data acquired during the field's shut-in period. The results of the material balance analysis were combined with seismic and well data to define the distribution of connected fractures across the field. Additionally, the material balance analysis was used to determine the connected hydrocarbon volume, the distribution of initial oil in-place and the relative hydrocarbon contribution from each production mechanism.
The field is covered by multi-azimuth 3D seismic and 43 vertical to highly deviated development wells, providing significant static and dynamic data for characterizing the distribution of connected fractures. Despite this high quality, diverse and field-wide dataset, prior modeling iterations struggled to sufficiently describe the production behavior seen at the well level. This has resulted in a major challenge to predicting the production behavior of new development wells and planning for reservoir management challenges. Capturing the complex interaction between production variables (including lithology, matrix versus fracture network, geomechanical stresses, reservoir damage and pressure depletion) at a field level instead of at an individual well level resulted in a unified static and dynamic model that reconciles all scales of observation.
This oilfield represents a unique reservoir characterization opportunity. The result is a key example of how iterative, integrated geological and engineering driven reservoir modeling can be used to inform the development in a complex, mature field. This case study provides an excellent analogue for the reservoir characterization of other fractured Basement fields and/or Basement-cover reservoir couplet fields in the early to late phases of their development.