The need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more demanding by the day. One of the most challenging and influential problems facing reservoir engineers is well placement optimization.
The North Kuwait field (NKF) consists of six fields containing four naturally fractured carbonate formations. The reservoirs are composed of relatively tight limestone and dolomite embedded with anhydrate and shale. The fields are divided into isolated compartments based on fault zones and supported by a combination of different fluid compositions, initial pressures, and estimated free-water levels. Due to natural complexity, tightness, and high drilling costs of wells in the NKF, it is very important to identify the sweet spots and the optimum well locations.
This paper presents two intelligent methods that use dynamic numerical simulation model results and static reservoir properties to identify zones with a high-production potential: reservoir opportunity index (ROI) and simulation opportunity index (SOI). The Petrel* E&P software platform was chosen as the integrated platform to implement the workflow. The fit-for-purpose time dependent 2D maps generated by the Petrel platform facilitated the decision-making process used for locating new wells in the dominant flow system and provided immense support for field-development plans.
The difference between the two methods is insignificant because of reservoir tightness, limited interference, and natural uncertainty on compartmentalization. At this stage, pressure is not a key parameter. As a result, unlike brown fields, less weight was given to simulated pressure, and SOI was used to select the well locations.
The results of this study show that implementing these workflows and obtaining the resulting maps significantly improve the selection process to identify the most productive areas and layers in a field. Also, the optimum numbers of wells using this method obtained in less time and with fewer resources are compared with results using traditional industry approaches.
Gomez, Ernest (Schlumberger) | Al-Faresi, Fahad A. Rahman (Kuwait Oil Company) | Belobraydic, Matthew Louis (Schlumberger) | Yaser, Muhammad (Schlumberger) | Gurpinar, Omer M. (Schlumberger) | Wang, James Tak Ming (Schlumberger) | Husain, Riyasat (Kuwait Oil Company) | Clark, William (Schlumberger) | Al-Sahlan, Ghaida Abdullah (Kuwait Oil Company) | Datta, Kalyanbrata (KOC) | Mudavakkat, Anandan (KOC) | Bond, Deryck John (Kuwait Oil Company) | Crittenden, Stephen J. (KOC) | Iwere, Fabian Oritsebemigho (Schlumberger) | Hayat, Laila (KOC) | Prakash, Anand (KOC)
The Burgan Minagish reservoir in the Greater Burgan Field is one of several reservoirs producing from the Minagish formation in Kuwait and the Divided Zone. The reservoir has been produced intermittently since the 1960s under natural depletion. A powered water-flood is currently being planned. The pressure performance of the reservoir has proved hard to explain without invoking communication with other reservoirs. Such communication could be either with other reservoirs through the regional aquifer of through faults to other reservoirs in the Greater Burgan field. Recent pressures are close to the bubble point.
A coarse simulation model of the nearby fields and the regional aquifer was constructed based on data from the fields and regional geological understanding. This model could be history matched to allow all regional pressure data to be broadly matched, a result which supports the view that communication is through the regional aquifer. Using this model to predict future pressure performance suggested that injecting at rates that exceeded voidage replacement by about 50 Mbd could keep reservoir pressure above bubble point. It was recognized that the process of history matching performance was non-unique. This is a particular concern in the context of this study because the model inputs that were varied in the history matching process included aquifer data that was very poorly constrained. To address this problem multiple history matched models were created using an assisted history matching tool. Using prediction results from the range of models has increased our confidence that a modest degree of over-injection can help maintain reservoir pressure.
This paper demonstrates the utility of computer assisted history match tools in allowing an assessment of uncertainty in a case where non-uniqueness was a particular problem. It also emphasizes the importance of understanding aquifer communication when relatively closely spaced fields are being developed.
Wu, JinYong (Schlumberger) | Banerjee, Raj (Schlumberger) | Bolanos, Nelson (Schlumberger) | Alvi, Amanullah (Schlumberger) | Tilke, Peter Gerhard (Schlumberger - Doll Research) | Jilani, Syed Zeeshan (Schlumberger Oilfield UK Plc) | Bogush, Alexander (Schlumberger)
Assessing the waterflood, monitoring the fluids front, and enhancing sweep with the uncertainty of multiple geological realisations, data quality, and measurement presents an ongoing challenge. Defining sweet spots and optimal candidate well locations in a well-developed large field presents an additional challenge for reservoir management. A case study is presented that highlights the approach to this cycle of time-lapse monitoring, acquisition, analysis and planning in delivery of an optimal field development strategy using multi-constrained optimisation combined with fast semi-analytical and numerical simulators.
The multi-constrained optimiser is used in conjunction with different semi-analytical and simulation tools (streamlines, traditional simulators, and new high-powered simulation tools able to manage huge, multi-million-cell-field models) and rapidly predicts optimal well placement locations with inclusion of anti-collision in the presence of the reservoir uncertainties. The case study evaluates proposed field development strategies using the automated multivariable optimisation of well locations, trajectories, completion locations, and flow rates in the presence of existing wells and production history, geological parameters and reservoir engineering constraints, subsurface uncertainty, capex and opex costs, risk tolerance, and drilling sequence.
This optimisation is fast and allows for quick evaluation of multiple strategies to decipher an optimal development plan. Optimisers are a key technology facilitating simulation workflows, since there is no ‘one-approach-fits-all' when optimising oilfield development. Driven by different objective functions (net present value (NPV), return on investment (ROI), or production totals) the case study highlights the challenges, the best practices, and the advantages of an integrated approach in developing an optimal development plan for a brownfield.
Blunt, Martin Julian (Imperial College) | Al-Jadi, Manayer (Kuwait Oil Company) | Al-Qattan, Abrar (KOC) | Al-Kanderi, Jasem M. (Kuwait Oil Company) | Gharbi, Oussama (Imperial College) | Badamchizadeh, Amin (CMG) | Dashti, Hameeda Hussain (Kuwait Oil Company) | Chimmalgi, Vishvanath Shivappa (Kuwait Oil Company) | Bond, Deryck John (Kuwait Oil Company) | Skoreyko, Fraser A. (CMG)
The Magwa Marrat reservoir was discovered in the mid-1980s and has been produced to date under primary depletion. Reservoir pressure has declined and is approaching the asphaltene onset pressure (AOP). A water flood is being planned and a decision needs to be taken as to the appropriate reservoir operating pressure. In particular the merits of operating the reservoir at pressures above and below the AOP need to be assessed.
Some of the issues related to this decision relate to the effects of asphaltene deposition in the reservoir. Two effects have been evaluated. Firstly the effect of in-situ deposition of asphaltene on wettability and the influence that this may have on water-flood recovery has been investigated using pore scale network modes. Models were constructed and calibrated to available high pressure mercury capillary pressure data and to relative permeability data from reservoir condition core floods. The changes to relative permeability characteristics that would result from the reservoir becoming substantially more oil-wet have been evaluated. Based on this there seems to be a very limited scope for poorer water flood performance at pressures below AOP.
Secondly the scope for impaired well performance has been evaluated. This has been done using a field trial where a well was produced at pressures above and substantially below AOP and pressure transient data were used to estimate near wellbore damage "skin??. Also compositional simulation has been used to estimate near wellbore deposition effects. This has involved developing an equation of state model and identifying, using computer assisted history matching, a range of parameters that could be consistent with core flood experiments of asphaltene deposition. Results of simulation using these parameters are compared with field observation and used to predict the range of possible future well productivity decline.
Overall this work allows an evaluation of the preferred operating pressure, which can drop below the AOP, resulting in lower operating costs and higher final recovery without substantial impairment to either water-flood efficiency or well productivity.
This paper presents a novel implementation for evolutionary algorithms in oil and gas reservoirs history matching problems. The reservoir history is divided into time segments. In each time segment, a penalty function is constructed that quantifies the mismatch between the measurements and the simulated measurements, using only the measurements available up to the current time segment. An evolutionary optimization algorithm is used, in each time segment, to search for the optimal reservoir permeability and porosity parameters. The penalty function varies between segments; yet the optimal reservoir characterization is common among all the constructed penalty functions. A population of the reservoir characterizations evolves among subsequent time segments through minimizing different penalty functions. The advantage of this implementation is two fold. First, the computational cost of the history matching process is significantly reduced. Second, problem constraints can be included in the penalty function to produce more realistic solutions. The proposed concept of dynamic penalty function is applicable to any evolutionary algorithm. In this paper, the implementation is carried out using genetic algorithms. Two case studies are presented in this paper: a synthetic case study and the PUNQ-S3 field case study. A computational cost analysis that demonstrates the computational advantage of the proposed method is presented.
The purpose of history matching is to achieve geological realizations calibrated to the historical performance of the reservoir. For complex geological structures it is usually intractable to run tens of thousands of full reservoir simulation to trace the most probable geological model. Hence the inadequacy of the history-matching results frequently leads to poor estimation of the true model and high uncertainty in production forecasting. Reduced-order modeling procedures, which have been applied in many application areas including reservoir simulation, represent a promising means for constructing efficient surrogate models. Nonlinear dimensionality reduction techniques allow for encapsulating the high-resolution complex geological description of reservoir into a low-dimensional subspace, which significantly reduces number of unknowns and provides an efficient way to construct a proxy model based on the the reduced-dimension parameters.
Polynomial Chaos Expansions (PCE) is a powerful tool to quantify uncertainty in dynamical system when there is probabilistic uncertainty in the system parameters. In reservoir simulation it has been shown to be more accurate and efficient compared to traditional experimental design (ED). PCEs have a significant advantage over other response surfaces as the convergence to the true probability distribution is proved when the order of the PCE is increased. Accordingly PCE proxy can be used as the pseudo-simulator to represent the surface responses of the uncertain variables. When the objective and constraints of a reservoir model is described by multivariate polynomial functions, there are very efficient algorithms to compute the global solutions. We have developed a workflow at which incorporates PCE to find the global minimum of the misfit surface and assess the uncertainty associated with. The accuracy of the PCE proxy increases with the additional trial runs of the reservoir simulator.
We conduct a two dimensional synthetic case study of a fluvial channel as well as a real field example to demonstrate the effectiveness of this approach. Kernel Principal Component Analysis (KPCA) is used to parameterize the complex geological structure. The study has revealed useful reservoir information and delivered more reliable production forecasts.
PCE-based history match enhances the quality and efficiency of the estimation of the most probable geological model and improve the confidence interval of production forecasts.
Nghiem, Long X. (Computer Modelling Group Ltd.) | Mirzabozorg, Arash (University of Calgary) | Chen, Zhangxin John (University of Calgary) | Hajizadeh, Yasin (Computer Modelling Group Ltd.) | Yang, Chaodong (Computer Modelling Group Inc.)
History matching of reservoir flow models based only on production data may not reveal deficiencies that affect future predictions. Incorporating saturation and temperature profile data that come from 4D seismic surveys in the history matching process can reduce the uncertainty of reservoir models for the prediction stage. We constructed a field reservoir model from which production history, saturation and temperature profile history were obtained. We started the history matching process with a base reservoir model, the petro-physical properties of which were substantially different than those of the field reservoir model. We propose a new methodology for matching the fluid and temperature profiles by adjusting reservoir petro-physical properties. In this methodology, some grid blocks in a reservoir model were selected judiciously to capture the overall saturation and temperature distribution profiles. In addition to well production data, we included the saturation and temperature profiles at these grid blocks as extra objective functions during the history matching process. The DECE optimization is used to reduce the objective function. We applied this method in a Steam Assisted Gravity Drainage (SAGD) process and matched the saturation and temperature profiles with an average error of less than 2%.
Subsea structures on the seabed may be impacted by free-floating or scouringicebergs. A drift-based Monte Carlo iceberg contact model was developed as partof the SIRAM (Subsea Ice Risk Assessment and Mitigation) program forcalculating iceberg impact risk for subsea structures on the northeast GrandBanks offshore Newfoundland and the Makkovik Bank on the Labrador Shelf. Themotivation for developing this model was to characterize the influence ofbathymetry (i.e., seabed orientation, ridges and basins) on iceberg interactionrates with subsea structures. Results were incorporated into a GIS-basedapplication to allow iceberg contact rates to be calculated for structures witha range of plan dimensions and elevations at various locations.
The subject Gas Field is located in the Sulaiman Fold Belt (SFB) in Pakistan. A realistic 3D static model was constructed for the challenging multiple reservoirs in the Field which included both clastics and carbonates. Four main reservoir horizons were modeled.
The steps involved in the Reservoir engineering analyses were: analyze PVT, well test, Static Pressure Data, and Core. The static pressure analysis helped define hydraulic compartmentalization in the field.
WHFP measurements were not available in the desired accuracy and density. A surface network model was used with plant inlet pressure as the primary constraint in order to obtain the required information. Satellite based elevation information was used to establish an accurate model with respect to pressure drop due to liquid hold up in pipelines.
The History Match in the field was performed on a Zone by Zone basis. In the absence of a 3D seismic cube, many of the faults in the field could not be interpreted, yet their presence was predicted by a closely matching Sand Box Model. This was an important clue which led to a useful approach regarding the location of simulation faults distributed in the entire field. An innovative approach was used in order to calibrate the size of sand lenses in one of the zones.
The final step was the forecasting and development of Optimal Scenario using Economic analysis. Many scenarios were tested, and the optimal scenario was identified. Maximum use was made of existing wellbores through re-completion, and new drilling was minimized. Furthermore, the impact of increasing the currently low Gas Price was tested. It was concluded that doubling of the gas price of the field would increase the NPV 3 times delay abandonment by 6 years.
The Gas Field is located in the Sulaiman Fold Belt (SFB). Eighteen (18) wells in all, those have been drilled in the Field. Currently 12 wells are producing Gas. The primary target horizons in Field are the Sui Main Limestone (SML) and Lower Ranikot (LRKT). However, the Dunghun Limestone and Pab Sandstone are also producing in some of the wells. The depositional sequence consists of clastic and carbonate succession. The stratigraphy of the reservoirs is strongly influenced by the structural evolution of the Sulaiman Fold Belt and initial rifting of the Indian Plate.
Blunt, J.D. (ExxonMobil Upstream Research) | Mitchell, D.A. (ExxonMobil Upstream Research) | Matskevitch, D.G. (ExxonMobil Upstream Research) | Younan , A.H. (ExxonMobil Upstream Research) | Hamilton, J.M. (ExxonMobil Upstream Research)
The ability to keep station is recognized as a key technical demand for yearround hydrocarbon exploration, development and production operations in thehigh arctic deepwater environment. Ice management has been used as ameans to improve station keeping ability in sea ice and extend operabilitybeyond the relatively short, ice free season in the Canadian BeaufortSea. The reliability of ice management is contingent upon accurate icedrift forecasting so decisions about operation suspension in the event of apotentially unmanageable ice intrusion can be conducted in a timelymanner. This paper demonstrates tactical level sea ice drift forecastingand proposes a model for free ice drift applicable to the shoulder seasons ofthe Canadian Beaufort Sea exploration window. Model calibration isdemonstrated with real ice drift time histories and the assumptions andlimitations of the approach are discussed.