Najmah-Sargelu Formations of Kuwait show considerable potential as a new unconventional hydrocarbon play and produces mainly from fractures. The key uncertainties which affect the productivity are the nature and distribution of permeable fracture networks, and the limits of oil accumulation.
This paper presents the results from whole-rock elemental analysis of three cored wells in UG field. The main objectives of this study are to use high-resolution elemental chemostratigraphy to gain a better understanding of the detailed stratigraphy and correlation of the Najmah-Sargelu Formations, to assess the chemo-sedimentology for determining the intervals of high organic content, to estimate the mineralogy of the sequence using an algorithm developed for an analog formation in North America; and to determine the most likely intervals to contain fractures, using a brittleness algorithm.
A clear chemo stratigraphic zonation is recognized within the Najmah-Sargelu Formation. The larger divisions are driven mainly by inherent lithological variation. The finer divisions are delineated by more subtle chemo stratigraphic signals (K2O/Th and Rb/Al2O3 ratios) and preservation of organic matter (high V, Ni, Mo, and U abundances). Zones of alternating brittleness and ductility are clearly identified within the interbedded limestones and marlstones of Najmah-Sargelu Formation.
Two unexpected but important features of the Najmah-Sargelu limestones were elucidated by the elemental data. Brittle, high-silica spiculites, with virtually no clay or silt, are more common than previously recognized from petrophysical logs and core descriptions in the upper Najmah limestones. In addition, the limestones adjacent to the spiculites tend to contain bitumen as pore-filling are recognized by the trace metal proxies. Ternary plots of V, Ni, and Mo differentiate the combinations of kerogen and bitumen present in the Najmah-Sargelu Formations.
The clarity and sensitivity of the chemostratigraphic signals are sufficient to enhance formation evaluation, and can also assist borehole positioning using the RockWiseSM ED-XRF instrument at wellsite.
Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, Voidage Replacement Ratio (VRR) and Net Present Value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field development activities on the environment while NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well placement optimization has been done mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of one (or close to one) during waterflooding.
A multiobjective approach incorporating NPV and VRR is proposed for solving the well placement optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase has the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights were used in the third phase to describe the relative importance of the NPV and the VRR and a comparison of how these weights affect the optimized NPV and VRR values is provided.
We applied the method to determine the optimum placement of wells using two sample reservoirs: one with a distributed permeability field and the other, a channel reservoir with four facies. Two evolutionary-type algorithms: the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE), were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary oil recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdiction with requirement for meeting a VRR of one.
Hydrocarbon exploration in the Arctic environment will very much depend onour ability to continuously track ice floes and forecast ice events that maygenerate dangerous loads on exploration and production infrastructure. Wepresent a first-of-its-kind computational framework which is centered aroundnear-real-time satellite imagery and incorporates real-time metocean data,providing automated analysis of such hazards in regions where moving ice ispresent. Our automated framework carries out several ongoing operations: icedetection and classification from satellite images, floe tracking from oneimage to the next, forecasting of floe trajectories beyond the observed tracks,and estimation of an uncertainty cone around the trajectory forecast. Weutilized the IBM InfoSphere™ Streams real-time analytics platform to deploy oursoftware, which made it possible for us to concentrate exclusively onprototyping algorithms, taking for granted the streaming infrastructure neededfor real-time data ingestion and flow between operators. Given our experiencedeveloping this prototype we conclude that a production-worthy, automatedtracking and forecasting capability is computationally feasible and within ourreach.
Numerical reservoir models are used to predict, optimise and improve production performance of the oil and gas reservoirs. History matching is required to calibrate reservoir models to dynamic behaviour of the reservoir. On the one hand, history-matching does not have a unique solution and multiple models can fit observation data, on the other hand, history-matching is a tedious and time-consuming trial and error process as it involves numerous reservoir simulation runs. Modern history matching techniques use optimisation algorithms aim at providing a set of good fitting models in an efficient time.
Many optimisation algorithms are applied in history-matching. Of them, Evolutionary Algorithms (EAs), inspired by natural evolution, do not use gradient information from the optimisation problem and only require the fitness function, usually defined as the sum of squares root deviation of model response from the observation data. Estimation of distribution algorithms (EDAs) are a novel class of EAs developed as a natural alternative to genetic algorithms in the last decade. To date, many EDAs are introduced which differ in the probabilistic model that guides the search process. Most of the EDAs are designed for discrete problems and require discretisation of search space when used for continuous problems, e.g. in history matching. In some cases, discretization error can be significant and deteriorate the search process.
Gaussian-based EDAs use characteristics of Gaussian distribution for multivariate continuous problems. i.e. they make use of mean and covariance matrix of the variables in the promising solutions to generate new solutions which fit better the observation data. In this paper, we introduce and for the first time apply four Gaussian-based EDAs to assisted history-matching of a standard synthetic case. We show our proposed algorithms may produce results more accurately and more efficiently for the continuous problems.
Chuai, Xiaoyu (China University of Petroleum-Beijing) | Wang, Shangxu (China University of Petroleum-Beijing) | Shen, Jinsong (China University of Petroleum-Beijing) | Chen, Wei (China University of Petroleum-Beijing) | Xiao, Mengxiong (China University of Petroleum-Beijing)