When developing and operating oil and gas fields, a large number of engineering decisions need to be taken. These decisions range from the very high-level such as recovery mechanism, facility sizing or well count to more detailed decisions such as individual well placements, well design and completion strategies. Typically, subsurface models play a role in informing these decisions by testing their effect on forecasted production and it is generally held best practice to test them against a wide range of subsurface conditions, a range that expresses the envelope of subsurface uncertainty. Whilst these commonly held objectives are more or less universal, selecting an appropriate subsurface modelling strategy (i.e. what to model & how to model it) to achieve these objectives usually generates more divergent views. Whilst there are always various valid modelling approaches available which are both geologically and numerically valid, a good modelling strategy pays close attention to the type of the specific decisions being taken for the project and the accuracy required to take those decisions.
To align these views, in decision-based modelling, business decisions and their timing were mapped to the models needed to make the estimates that inform them. Dialogue between the disciplines established the accuracy required for these estimates, and these discussions often revealed surprising opportunities to simplify processes and accelerate delivery. The outcome is a common view on a decision-driven modeling strategy that enables business decisions to be taken efficiently. This strategy, and the subsequent delivery plan that is developed from it is seen as a key to success.
This paper describes the decision-based approach taken in the subsurface modelling required to support quality development decisions for the development of two of KOC's Heavy oil fields through the Enhanced Technical Service Agreement (ETSA) between Shell and KOC. In this particular application of the process, the suite of models to be built has been mutually agreed at the start by the integrated development team through a structured collaborative workshop, or Model Framing Event. A fullfield and a sector model for 2 different fields are discussed to exemplify the link between required decision and supporting modelling approach.
Dashti, L. (Kuwait Oil Company) | Filak, J. M. (Kuwait Oil Company) | Bond, D. J. (Kuwait Oil Company) | Banagale, M. R. (Kuwait Oil Company) | Al-Houti, R. A. (Kuwait Oil Company) | Luneau, B. A. (Consultant) | Molinari, D. (IFP Middle East Consulting)
The Wara reservoir is one of the main producing formations of the giant Greater Burgan field. It has been on production under natural depletion for many years. A massive water-flood of this formation has recently commenced. This was preceded by a large-scale pilot water flood the aims of which included enhancing reservoir understanding. This paper describes how historical data, including data from the large-scale pilot, were used to construct representative part filed models.
The area of the pilot water flood has significant volumes of data, including core and log data and dynamic data such as pressure transient data, interference tests, tracer tests and cased-hole logs. These provide valuable information for reservoir characterization.
The Wara formation was deposited in a tidally influenced fluvio-deltaic environment where sand continuity is complex. There was a desire to develop realistic geological and simulation models that accounted for our understanding of Wara geology and were consistent with the large volumes of surveillance data.
A major challenge was the choice of an appropriate area for the part field model. This was chosen so as to allow water influx into the area of interest over the life of the field to be accounted for and to allow relatively simple boundary conditions to be applied.
Geological models were constructed using object based techniques. These models used reservoir rock types that were developed to broadly match permeability-height estimates from pressure transient data.
The geological models were not guaranteed to account for the sand connectivity inferred form the surveillance data. A streamline based screening technique was used to exclude models that did not broadly capture the interpreted connectivity.
Dynamic simulation models were then developed and conditioned to data using conventional assisted history matching techniques. At this stage, some sensitivities related to boundary conditions were explored. Sand connectivity was not varied at this stage.
Some examples are given as to how the resulting conditioned models have been used to address questions about expected future reservoir performance. Specifically questions related to proposed well spacing and pattern type are discussed.
This paper describes a novel approach to developing models that are geologically realistic and are consistent with the interpretation of reservoir connectivity from a range of surveillance data. This involves using a streamline based screening tool before using assisted history matching techniques. Such an approach can be applied to both part and full field models.
The challenges of using such an approach with part field models are described. Some guidance is given to know when it would be appropriate to try to develop and condition part field models.
Chan, Keng Seng (Petronas) | Masoudi, Rahim (Petronas) | Karkooti, Hooman (Petronas) | Othman, Mohamad (Petronas) | Tan, Chee Phuat (Schlumberger) | Anis, Apollinaris Stefanus (Schlumberger) | Press, David (Schlumberger) | Setiawan, Ngurah Beni (Schlumberger)
A method of constructing high productivity and high wellbore dynamic stability wells has been developed for EOR field development application. It integrates a heuristic reservoir engineering modelling method for determining optimum drainage points with high dynamic flow and a 3D coupled reservoir geomechanical modelling method for identifying low sanding propensity regions within the entire reservoir. The reservoir geomechanical method couples dynamic reservoir modelling with geomechanical modelling. It can account for in-situ stress changes associated with reservoir pressure change, and predict any potential geomechanical-related physical events for the remaining life of the field. Correspondingly, the generated drainage map and 3D sand production critical drawdown cubes can then be combined to identify global optimum well placement locations within the reservoir, layer by layer.
Drainage points selected by this heuristic reservoir modelling method can be correlated with their respective EUR (Estimated Ultimate Recovery) values, while the 3D critical drawdown cubes can identify reservoir regions with low sanding propensity. This combined approach can therefore lead to the development of multi-layer commingle wells having various angles of reservoir penetration, for optimizing well productivity and EUR value without the requirement of sand control. Illustrated by a case study in brownfield reservoirs, a non-linear well trajectory which maximizes reservoir fluid contact in a prolific sand layer can be designed without any sand control completion for sanding mitigation through optimization and management of production plan.
Chuah, Bengsoon (PETRONAS) | Soni, Sumit (PETRONAS) | Jalan, Shlok (PETRONAS) | Kartooti, Hooman (PETRONAS) | Fauzi B. Tg. A. Hamid, Tg. M. (PETRONAS) | Chan, Keng Seng (PETRONAS) | Masoudi, Rahim (PETRONAS)
It shall never be over-emphasized that the balance of cost and value is very crucial in determining the commercial feasibility of a field development or redevelopment project. The values are generated by wells that could fetch higher productivity and could effectively drain out larger reservoir hydrocarbon fluid volume. Well drilling and completion costs and their surface production supporting facilities costs have been steadily increasing in recent years. Subsurface engineering studies shall therefore also focus on optimizing the well placement and orientation, the well type and completion selection, the life-cycle control of well inflow and outflow, with the minimum well count to yield higher values.
This paper entails various methodologies of selecting drainage and injection points by combining the remaining mobile oil, current productivity, and current pressure depletion maps constructed from history matched reservoir simulation models. Base on predominant drive mechanisms in the reservoirs studied, governing parameters were coupled in 3 property groups and normalized individually. A known heuristic approach was also adapted to construct a Simulated Opportunity Index (SOI) map. A correlation between the SOI and recoverable reserve (EUR) was established by simulation prediction runs for each drainage or injection point selected, sand by sand in the studied reservoirs. The studied reservoir cases including a vast thin oil-rim reservoir, a huge multiple stacked reservoir, a complex compartmentalized reservoir, and a prolific deep-water reservoir.
Clustering the selected drainage and injection points in several sands to further maximize the well productivity, optimization of the inflow control for the selected commingled sands, and the design of cost effective completions, shall be addressed later sequentially in separate papers.
The technical challenge is getting difficult as fields are reaching maturity. The complexity and uncertainty of the field require a detail understanding of both reservoir characteristics and facilities performance in order to identify and optimally exploit the field potential. In multi layered reservoirs, substantial reserves is located in minor reservoirs that demand innovative solution for cost effective redevelopment. The wells drilled in later part of the brown field especially require maximizing reservoir contact, higher well productivity for higher recovery to justify the well cost. Various well architecture options with elaborated smart bottom-hole devices is being deployed to control drawdown and sand production. To achieve maximum recovery with suitable well architecture, meticulous selection of optimum drainage and injection point is critical to boosting recovery from a brown field. Drainage point can be selected once confidence over complex remaining oil evaluation is established. Qualitative and quantitative methodologies ranging from surveillance and performance evaluation to 3D models are used to establish drainage and injection points in matured or brown reservoirs.
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.
North Kuwait had discovered several deep reservoirs during 1960s which were not considered for development production till 2010 because of higher priority given to the major reservoirs such as Mauddud and Burgan. Zubair and Ratawi are two such new reservoirs, taken up for fast track development since last 2 years.
In order to achieve the production target for the asset, ZU/RT need to be brought to production. A complete evaluation of all geological/ Geophysical data was done for all penetration through ZU/RT. All testing results available during the exploratory wells were comprehensively gathered and evaluated. Based on the data, fast track well release, drilling & completion were initiated. The objective was to gain production as well as data to reduce the uncertainty for further development and reservoir modeling work. The initial results indicated tremendous challenges in terms of fluid regime as well as reservoir rock properties.
The production, at the beginning of the project, was zero, which has now been increased to about 20 MBOPD within a year. Integrated team work and calculated risks helped to achieve this milestone.
The paper aims to share the challenges and the lessons learnt during the production delivery from the new reservoirs in North Kuwait.
Geological Background/ Setting
Sabiriyah Zubair is a minor reservoir of clastic type, which was on low priority for development/ production since discovered druing 60s.. This is a relatively small reservoir in terms of size and potential, as compared to other major reservoirs in Sabiriyah field viz. Mauddud & Burgans. However, the accomplished intial tests at two wells (SA-00271 and SA-0278) indicate that the reservoir has mobile oil and can participate to contribute to NK overall production. Currently 7 wells are flowing to Gathering centre at total rate of about 10 MBOPD.
The Mauddud reservoir in the Greater Burgan field is a thin, carbonate reservoir containing light oil in a 10 to 20-feet (ft) target zone with "good" porosity. Matrix permeability is low, and natural fracture density can be variable in this reservoir. Thus, this reservoir must be exploited using horizontal wells. In the early 1990s, 16 horizontal wells were drilled in this reservoir. Five more horizontal wells have been drilled in 2005 and 2006 in an effort to scope out the long-term potential of this reservoir. However, only three of these five new wells had a production history of a few months that could be used in our history-matching effort. Thus, the history-matching effort concentrated on 19 wells (16 old plus 3 new wells).
In conjunction with the drilling of recent horizontal wells, a comprehensive reservoir characterization program culminating into a full-field reservoir simulation model has been completed. The 24-million cell geological model was scaled up to a 9-million cell model at a 164-ft by 164-ft areal grid level to properly incorporate flow characteristics of horizontal wells into the simulation model. Matrix permeability of the scaled-up model was enhanced by using a unique process based on analytical solutions for short fractures and fracture density/orientation mapping for the entire field. This reservoir simulation model has been history-matched for the 13-year production history of 16 1990s horizontal wells along with a production history of a few months for 3 new wells using only a global-permeability multiplier and water relative-permeability curve shape modification. This model has been used in the forecast mode to assess long-term field development opportunity for the Mauddud reservoir. Primary depletion results show that horizontal wells drilled in an intelligent manner in this difficult reservoir hold the key to economic development of this reservoir.
Mauddud reservoir in the Greater Burgan field is a thin, carbonate reservoir containing light oil in a 10-20 ft target zone with "good?? porosity. Matrix permeability is low and natural fracture density can be variable in this reservoir. Thus, this reservoir must be exploited using horizontal wells. In the early 1990's, 16 horizontal wells were drilled in this reservoir. Five more horizontal wells have been drilled in the last 2 years in an effort to scope out the long-term potential of this reservoir.
In conjunction with the drilling of recent horizontal wells, a comprehensive reservoir characterization program culminating into a full-field reservoir simulation model has been completed. The 24 million cell geological model was scaled up to a 9 million cell model at a 50 m x 50 m areal grid level to incorporate flow characteristics of horizontal wells properly in the simulation model. Matrix permeability of the scaled-up model was enhanced using a unique process based on analytical solutions for short fractures and fracture density/orientation mapping for the entire field. This reservoir simulation model has been history-matched for the 13-year production history of 19 horizontal wells using only a global permeability multiplier and water relative permeability curve shape modification. This model has been used in the forecast mode to assess long-term field development opportunity for the Mauddud reservoir. Primary depletion results show that horizontal wells drilled in an intelligent manner in this difficult reservoir hold the key to an economic development of this reservoir.
This paper highlights integrated geological, geophysical, rock mechanics, petrophysical, and reservoir simulation work required to assess the potential of a difficult reservoir characterized by low matrix permeability, thin pay, variable fracture density, and lack of aquifer support. This paper also presents a "stand-alone?? full-field, history-matched, parallel simulation model of the Mauddud reservoir for the first time.
Tahe, one of the largest scale oilfields which are discovered in western China recently, in which Ordovician carbonate as the main storage, characterized with deeply embedding and various storage type, mainly are cavities and fractures. It is difficult to describe as its serious heterogeneity and there is rare technical reference regarding the reservoir modeling methodology.
Commonly, the geology modeling is based on well logging and seismic data, and the stochastic method is reasonable applied. However, Tahe oilfield has very limited seismic data and poor well logging interpretations, especially for the fracture-cavern section. Furthermore, as it is controlled by paleukarst, the stochastic geological method, which is widely used in the ordinary sedimentary, is unacceptable to Tahe. The volumetric reserves calculation, which is suitable to sandstone but is not applicable to the fracture-cavity reservoir either, as it can not be proved in the severe anisotropy fracture-cavity carbonate formation. Furthermore, the concept of porosity and saturation is less meaningful in this kind of reserves. As a result, there is no applicable modeling method to describe the geological characters of the Tahe carbonate reservoir.
A new methodology which is characterized by the fracture-cavity volume is presented in this paper. Accordingly, a new reservoir modeling and a new reserve calculation is built up. Mainly from geology, combined with static and dynamic data, a quantified 3-D reservoir model of Ordovician reservoir is formed, and this modeling methodology is proved practically by the results of history match from the streamline numerical simulation. The result will technically support the exploitation of Tahe oilfield.
Most reservoirs in Tahe Oilfield are Carbonate rocks, which is controlled by karst, and with low matrix porosity and without oil storage capability. As the main storage space and seepage path, fracture-cavity and redeposit erosive cavities are well developed. Erosive vuggy, solution cavity and fracture are combined together in different modes, which form three main reservoir types: fracture, fracture-vuggy and fracture-solution cavity [1-4]. In field, this kind reservoir is generally divided into two types, type I, which includes fracture-vuggy and fracture-cavity, while type II is mainly composed of fracture with undeveloped karsts. Strong anisotropy results in more difficulty in describing carbonate reservoir .
Previous reservoir modeling was based on logging data and seismic data, applied the stochastic method. However, the Tahe oilfield is deeply buried, which results in the limitation of seismic data interpretation and inaccurate log data judgment for fracture-cavity payzone. On the other hand, it is so unacceptable that the Tahe oil field, which sedimentary is controlled by paleo-karst, is modeled by the stochastic geological method that is used to apply for the sedimentary faces. Therefore, geological reservoir model of Tahe oilfield based on the present data and method could not reflect the actual situation of the reservoir [6-9].
In addition, the reserve was calculated by volumetric method as the following steps: first, determine the oil and gas containing area on T70 structural which drawing of seismic reflecting layer, then determine an oil basis for calculation, calculate the effective thickness of the oil layers above the oil basis, calculate the effective porosity by effective thickness average weight, and achieve the average oil saturation of pores with the effective volume weight. Fracture oil saturation was achieved by referring to the value adopted by other foreign oilfields, and the ground crude oil density was achieved by averaging of actual analytical materials. This method did not take the anisotropy of the formation into consideration, and more importantly, the difference between the recoverable reserve of formation type I and II could not be presented while combining the reserve in the fracture-cavity of formation type I and II for calculation, so that the distribution of the reserve could not be reflected at all.