Mahakam Block has been in operation for nearly half a century with cumulative production of approximately 20 trillion cubic feet of gas and 1.5 billion barrels of oil. Mature field challenges have become more evident as portrayed by declining production, more complex surface constraints, more challenging profitability of new projects and decreasing resources of new wells, which are also exacerbated by external factors such as volatility of oil and gas prices. Despite the aforementioned challenges and complexity in terms of operating numerous fields with different characteristics, Mahakam is currently still one of the biggest producing blocks in Indonesia. The success of sustaining production and prolonging the life of Mahakam is the result of continuous innovations, improvements and optimizations on various aspects over the years. Subsurface innovative ideas by restudying and redefining geological concepts has led Pertamina Hulu Mahakam (PHM) to drill step-out wells in Handil, Tunu, South Mahakam and Sisi Nubi fields that deliver positive results and open new opportunities. In the non-subsurface aspect, Indonesia's first Plan of Development that combines higher and lower value projects across fields called OPLL (Optimasi Pengembangan Lapangan-Lapangan) was initiated in order to develop fields with marginal value and to achieve economy of scale. Moreover, Capital Expenditure (CAPEX) optimization through evolution of platform design, well architecture and sand control method is crucial for exploitation of targets with lower resources over time. PHM has also launched CLEOPATRA (Cost Effectiveness and Lean Operations in Mature Asset), later renamed to LOCOMOTIVE-8 (Low Operations Cost of Mahakam to Achieve Effectiveness and Efficiencies), to achieve Operating Expenditure (OPEX) efficiency through various initiatives driven by each entity. Due to cost of money, budget accuracy is as important as expenditures reduction meaning that more detailed and deterministic budget estimation is necessary. In addition to optimizing cost structure, PHM strives to carry out gas commercialization efforts to improve revenue streams. In this rapidly changing era, especially for Mahakam, paradigm shift becomes highly critical. Changes in the structure and size of organization is essential to adjust with business dynamics. Adaptive organization structure is performed through digitalization and competency improvement to reduce repetitive tasks and increase productivity per capita. Cooperation between neighboring companies brings mutual benefit by sharing rig, transportation means, and pipeline network systems. Mutual benefit opportunity is also available between the company and Indonesian government by amendment of fiscal terms with the aim to enable the execution of sub-economic projects. Ultimately, one effort alone may be insignificant, but the combination of all of the efforts will be the key to the continuation of Mahakam story.
Tunu is a mature giant gas field, located in swamp area of Mahakam delta. It covers an area of 75 km long and 15 km wide with enormous multi-layer sand-shale series deposited within a deltaic environment. The production commenced in 1990 and peaked in 1999 (1.5 Bcfd yearly average), with current production around 600 MMscfd, and nearly 1,200 wells drilled from 34 platforms. As the field is entering into the late stage of its life and number of new wells being drilled is decreasing, the field potential is now quite dependent on the existing perforation portfolio. Owing to the significant number of wells, with multi-layer reservoirs encountered by each well, the perforation portfolio needs to be managed in an efficient and detailed manner. An intensive well review has been performed, involving more than 9,000 reservoirs located in over 800 active wells. It commenced with an evaluation of perforation gain and its associated risks at reservoir level, which are then progressively summarized into higher levels; well, platform, and field. Perforation sequences are then defined for each well following a bottom-up perforation strategy while also adapting the risks of each reservoir. Ultimately, an organized reservoir chart is constructed where we can dynamically surveil the field perforation portfolio. Results of this intensive well review have enabled the shift of production forecast methodology from statistical toward deterministic approach. In the new approach, for instance, perforation sequences and time interval between the sequences in each well are deterministically determined on a well by well basis according to the aforementioned reservoir chart. Compared to the previous methodology, this new approach yields a better representation of the actual operations. Another area benefited by the results of the intensive well review is the optimization of well intervention planning. The identified workload is grouped together based on the remaining perforation portfolio of each platform while also respecting their location to define several well intervention clusters. Implementation of this clustering system optimizes perforation planning which reduces the time spent by perforation barges to travel from one platform to another, thus allowing more time for perforation.
A giant gas field that covers 75 km in length and 15 km in width has been producing since 1990 from approximately 1,200 wells which are located in 34 platforms. Deposited within a deltaic environment with enormous multi-layer sand-shale series, the wells undergo commingled production with an average of more than 30 reservoirs per well. With a total of approximately 700 perforation jobs included in more than 4000 well intervention jobs per year, the field is considered as the most complex field in the PSC Block in terms of operations. Prioritization of these perforation jobs are based on the perforation gain of each job. Therefore, properly estimating the perforation gain is crucial in order to efficiently and effectively manage and prioritize well intervention jobs. Hypothetic approaches, for instance productivity index driven from Darcy's equation, may not be straight-forward due to incomplete and imprecise data measurement. Overwhelming operations workload in the field limits the number of data acquisition jobs performed. Consequently, required data to estimate perforation gain such as skin, pressure and drainage radius becomes limited. An alternative approach using artificial intelligence called fuzzy logic was introduced. Being a soft-computing pattern recognition method that allows imprecise input to yield output, fuzzy logic fits well with the nature of high uncertainty in geosciences data. The one-year study is conducted on reservoir basis using well monitoring results to split well level gas rate into individual reservoir gas rate. In order to ensure that proper data are incorporated in the model training, processes of data filtering must be undertaken. Therefore, implementing fuzzy logic to estimate perforation gain includes 3 main steps: (1) Preparing and Filtering Training Data Set; (2) Building the Fuzzy Model; and (3) Performing Blind Test. After series of trial and error process, the model has reached its minimum error without compromising sense of engineering and generality. The fuzzy model results in 960 fuzzy rules and 5 input parameters: netpay, porosity, drawdown, mobility and water risk. Afterwards, the blind test shows that the resulting output from fuzzy logic correlates well with the realized gas rate both on reservoir level and well level, with maximum R-squared value of 0.7. The study is limited within the scope of current best practice for unperforated reservoirs and further study would be required to estimate the perforation gain from unconventional perforation methods and re-perforations. This method of estimating perforation gain using fuzzy logic has been implemented on daily basis with the aim to improve the efficiency and effectiveness of managing and prioritizing well intervention jobs in such a complex environment.
The studied field was discovered in 1974 and has been in operation for nearly 50 years. Being deposited within a deltaic environment with enormous multi-layer sand-shale series, the field is vertically divided into dozens of geological layers. Previous reserves estimation method of manually performing dynamic synthesis followed by volumetric calculation per layer basis has become less preferable amid increasing drilling and well intervention activities. Meanwhile, reservoir simulation is also inapplicable for reserves estimation due to the field's subsurface complexity. This paper shares an approach to automate well correlation and dynamic synthesis process by integrating static and dynamic data into Visual Basic for Application (VBA) based tool in order to efficiently estimate reserves and accelerate candidate selection for new well drilling and well intervention. Performing dynamic synthesis on a certain reservoir within a well of interest involves estimation of latest fluid status, pressure, water risks, recovery factor, and drainage radius by analyzing recent static and dynamic data from surrounding wells. As the static data and dynamic data from hundreds of existing wells are available in separate databases, the study commences with collecting, updating, filtering, organizing and integrating data into one reliable database. Afterwards, the automation tool is designed to quantitatively mimic the logics of performing well correlation and dynamic synthesis using weighting factors that characterize the reliability of data based on 3 parameters: distance to the well of interest, recentness of data, and sand similarity. Since these parameters have distinctive influence depending on the dynamic property being estimated, influence factors are introduced for each parameter and each dynamic property through trial & error process. Combining weighting and influence factors with available data results in the estimated dynamic properties that become input to volumetric calculation of reserves. In order to validate the model and tool, blind tests are carried out using data from recently drilled wells which are not included in generating the estimation. Pressure blind test shows good correlation between predicted and realized values, meaning that the tool is able to predict pressure accurately. Reserves estimation blind test also shows satisfying results both at reservoir and well level. Following successful blind tests, the tool has been utilized to aid engineers in proposing new wells and well intervention candidates. As a result, 8 wells were able to be proposed in a timely manner for the sanction of future development. This paper presents an efficient, novel and robust approach in estimating reserves for heterogeneous fields where reservoir simulation is inapplicable. The tool also allows straightforward update when adding data from new wells. However, further study is required for estimation in less dense areas where the amount of surrounding wells and data are insufficient.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.