Optimizing multiple assets under uncertain techno-economic conditions and tight government policies is challenging. Operator needs to establish flexible Plan of Development (POD)s and put priority in developing multiple fields. The complexity of production and the profit margin should be simultaneously evaluated. In this work, we present a new workflow to perform such a rigorous optimization under uncertainty using the case study of PHE ONWJ, Indonesia. We begin the workflow by identifying the uncertain parameters and their prior distributions. We classify the parameters into three main groups: operations-related (geological complexity, reserves, current recovery, surface facilities, and technologies), company-policies-related (future exploration plan, margin of profit, and oil/gas price), and government-related (taxes, incentives, and fiscal policies). A unique indexing technique is developed to allow numerical quantification and adapt with dynamic input. We then start the optimization process by constructing time-dependent surrogate model through training with Monte Carlo sampling. We then perform optimization under uncertainty with multiple scenarios. The objective function is the overall Net Present Value (NPV) obtained by developing multiple fields. This work emphasizes the importance of the use of time-dependent surrogate approach to account risk in the optimization process. The approach revises the prior distribution with narrow-variance distribution to make reliable decision. The Global Sensitivity Analysis (GSA) with Sobol decomposition on the posterior distribution and surrogate provides parameters’ ranking and list of heavy hitters. The first output from this workflow is the narrow-variance posterior distribution. This result helps to locate the sweet spots. By analyzing them, operator can address specific sectors, which are critical to the NPV. PHE ONWJ, as the biggest operator in Indonesia, has geologically scattered assets, therefore, this first output is essential. The second output is the list of heavy hitters from GSA. This list is a tool to cluster promising fields for future development and prioritize their development based on the impact towards NPV. Since all risks are carried by the operator under the current Gross Split Contract, this result is advantageous for decision-making process. We introduce a new approach to perform time-dependent, multi-asset optimization under uncertainty. This new workflow is impactful for operators to create robust decision after considering the associated risks.
Offshore North West Java is a mature oil and gas field located in northern part of Java Island. Most of the wells are producing with gas lift system from the abundant source of gas in the field. Through forty years production life of this field, the conventional gas lift spacing design is found out to be not optimum. Many unloaders at the upper part of the completion aren't necessary during its production stage and usually will be changed into a dummy valve during gas lift valve redesign (GLVR) operation. These excessive number of valves may cause many problems, such as limited gas that can be delivered through the orifice, higher probability of valve and installation failure, and many more. These conditions will lead to un-optimum production rate. A new innovative method of gas lift spacing design is proposed to solve the problem by optimizing the number of gas lift valves installed in the completion. In conventional gas lift spacing design, completion fluid level is often represented static at the surface using a static fluid model. In fact, completion fluid level tends to change over time due to fluid infiltration into the reservoir. By emphasizing this fluid infiltration into the reservoir, equalized method is created. This equalized method alters the spacing design starting depth from the surface into the depth which equalized condition between bottom hole and reservoir pressure is reached. By combining Darcy' law and hydrostatic pressure formula, a new equation is derived. It is able to forecast the time needed to reach the equalized depth and also the depth itself. To verify the newly developed method, a case study of Well-X is presented. To enhance Well-X production, a gas lift system is required. Using a predetermined compressor pressure, the conventional gas lift spacing method yields a total of eight unloader valves. In contrast, the equalized method reduced the number of unloader valves required to a total of four. The example has proved that the equalized method is not only able to reduce the chance of failure in the installation, but it also results in a higher gas lift operating pressure, higher gas injection capacity, and in the end, 5.6% of higher oil production rates obtained compared to the conventional method. The novelty of this paper is an optimized gas lift spacing design by using the equalized method. For further implementation, this method can be applied in most oil well cases with gas lift system, for a better economic profit.
We present a simple analytical solution to diagnose gas production under compaction. This solution scales production profile of different wells and collapses them into a single general curve. The curve will later serve as the "learning" function for physic-based machine-learning prediction. A rapid growing flood of big data in the oil and gas industry reveals a substantial opportunity to the better understanding of hydrocarbon reservoir. With machine learning, one can turn a numerous amount of data to predict future production and determine field economics. However, the quality of the prediction from machine learning is dependent on the learning function selected that most of the time does not concatenate any physical aspects of the problem. In this paper, we offer a better machine learning with a physics-based function to estimate future gas production under severe compaction. We construct a physic-based master curve by solving the coupled Darcy-Biot equation for vertical gas well under reservoir compaction. We assume that the flow is radial and the porosity is transiently changing by the reduction in pore pressure due to gas production. Finally, we reduce the complexity of the coupled non-linear equation to two scaling optimization parameters: a mass scaling factor to scale the recovery factor and time scaling factor to scale the diffusion time. We verify our model with a field case from KLX field, Indonesia. This gas field produces an enormous amount of gas with subsidence as the side effect. The subsidence was identified by knowing the change in platforms level. By collapsing the production profile of all existing wells into a single master curve, we capture the universal scaling parameters that represent the behavior of gas flow under reservoir compaction. Furthermore, we can substitute the resulted master curve as the learning function for to the machine-learning model to predict and diagnose other fields in the future that undergo the same phenomena. We find that reservoir compaction leads to a higher recovery factor of gas for a long term. However, the high subsidence rate is not a favorable condition for the offshore field as the production facilities on the platform will submerge under sea level in a matter of years. Thus, the field owners must consider some subsidence mitigations such as injection and maintaining critical production rate. Our novelty is to produce a general scaling to describe gas production under compaction, which is later useful for the development of our machine-learning process to simplify the prediction process, not involving extensive and expensive numerical simulation.
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